Generative AI: what it is, how it works, applications and strategies

Create an entire article in seconds, a complete social campaign just to launch or a perfect image simply by describing it in words. This is not science fiction, but it is the concrete potential ofgenerative AI, the technology that is redefining the boundaries of creativity and productivity in areas such as SEO, digital marketing, and beyond, and that has now entered powerfully into our daily activities. Indeed, from being a niche tool, the preserve of developers and researchers only, generative AI has quickly transformed into an accessible and essential resource for those working with online content. Generating text, images, video, music and even code is now a possibility within everyone’s reach, in real time and with extraordinary levels of personalization. However, behind this enormous potential lie essential questions. How does it really work? What benefits does it offer in practice? And is it safe to rely on a tool that, if not properly guided, can run into errors or hallucinations? In this guide we will explore every aspect of generative AI, to clarify what it is and to understand how to make the most of it through practical strategies, with a special focus on advanced tools such as those integrated in SEOZoom. We will always stay connected to reality, avoiding easy enthusiasm or visionary tones: the goal is to provide concrete value to those who want to use these tools strategically, consciously and safely.

What is generative AI

Generative AI is a branch of artificial intelligence designed to create new content, such as text, images, audio, video or code, from existing data. Unlike other AI models, which merely analyze, classify or predict data (discriminative models), this technology autonomously generates original output, responding to specific user-supplied commands or input, called prompts – which is why it is precisely called “generative.”

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In practice, generative AI “learns” from the datasets it has been trained on-a corpus of existing content-and then combines the information to produce new results that were not present in that original data. This process is not simple replication, but reworking based on statistical probability and learned patterns. It is because of this capability that a text generator can compose an article from scratch or that software such as DALL-E can transform a phrase such as “An elephant reading a book under a tree, drawn in cartoon style” into a new image .

Among the best-known examples are Large Language Models (LLMs), such as OpenAI’s GPT-3 and GPT-4, which are designed to generate elaborate text content based on specific inputs. Other examples include image generation software, such as Stable Diffusion and MidJourney, and audio and video creation tools such as RunwayML. In each case, the bottom line is that AI is able to create something new by interpreting data and instructions with an unprecedented level of realism and consistency.

Understanding generative AI: a simple explanation

Generative AI is thus a technology that can create new and original content, such as text, images, video, music, or code, from data it has been trained on. These systems work using a process based on mathematical and statistical models, which analyze patterns in pre-existing databases to predict and construct new output consistent with the context provided by the user.

To use an evocative expression of our CTO Giuseppe Liguori, we can imagine generative AI as a “stochastic parrot”: it does not really understand the information it produces and does not “think” in the human sense of the term, but intelligently repeats and processes what it has learned from training data. The model simply predicts what is most likely to follow in a given sequence, using statistical correlations between words, pictures, or other types of data.

For example, when faced with the prompt “Write an article on how local SEO works”, a model such as GPT-4 succeeds in generating consistent text because of its ability to associate the context of local SEO with information from its training data. Similarly, an image generator such as DALL-E can provide a graphical representation from a textual description-for example, “A cat on a cloud in the moonlight, cartoon style”.

The name “generative” comes from the fact that this AI does not merely describe or classify existing information, as discriminative models do, but creates new content by uniquely combining pieces of existing data.

In short, generative AI is not magic, but an answer to the marriage of technological advancement and creativity-a powerful tool that always requires human control to reach its full potential.

How generative AI works

Behind the creative process of generative AI is a combination of advanced technologies, particularly deep neural networks, pre-trained models, and an architecture called transformer. These systems work in synergy to analyze input and produce content that is coherent, refined, and adapted to the context required by the user.

The fundamental principle is that of probabilistic prediction. Each word, pixel, or musical note is generated based on the probability that that specific sequence “makes sense” in context. For example, if we provide a prompt such as “Write a description of the sunset”, the model will analyze the data it has been trained on to choose the most suitable words, such as “hot,” “red,” “horizon line,” and so on, putting them in a logical order.

The crucial step in the operation of generative AI is based on deep neural networks, mathematical structures that are inspired by the operation of the human brain, which have as essential components:

• Pre-trained models. AI is initially “trained” with giant datasets, such as text, images or information from the Internet, to learn patterns, language structure and meaningful associations between words or visual elements.
• Transformers (Transformers). A revolutionary technology introduced in 2017 with the paper “Attention is All You Need”. Transformers allow AI to understand the context of sentences in their entirety, thanks to the self-attention mechanism, which analyzes the relationships between even very distant words within a text.
• Word embedding. Words and concepts are represented in a multidimensional space where related terms (such as “sun” and “light”) appear close together, allowing the model to construct coherent and meaningful sentences.

These components converge to allow contextualized production. For example, when a model like GPT-4 receives input, it divides the text into tokens-small fragments of language-and uses the transformer to “understand” not just that word, but the overall meaning of the sentence. This enables generation that appears natural, fluid and often indistinguishable from human writing.

In addition, tools such as diffusion models (e.g., Stable Diffusion) exploit similar logic to produce images. In this case, data are “ruminated” and then gradually “cleaned up” to create a crisp, realistic image from features learned during training.

Thus, generative AI not only responds to what we ask, but processes it creatively and scalably, representing a key step in the process of automating human creativity. It is precisely this ability to generate original outputs that has made it one of the most relevant technologies of our time.

What generative AI is good for

We are dealing with an extremely versatile tool that can meet a wide range of creative and operational needs. Its ability to create content on demand, both textual and visual, has made its use possible in so many different areas, including (and going beyond) SEO and digital marketing.

For example, it can be used to:

• Improve productivity in content creation. From copywriting to visual design, generative AI tools help copywriters, graphic designers, and content creators reduce time to completion, allowing them to go from concept to operation in just a few clicks.
• Personalizing the user experience. In chatbots, e-Commerce and personalized services, generative AI systems can create dynamic responses or tailored content. Think, for example, of chatbots that offer empathetic and contextualized responses or sites that generate personalized recommendations.
• Generate simulations and synthetic data. Useful for fields such as scientific research and medicine, where AI can create simulated diagnostic images or design drugs by synthesizing original molecular structures.
• Support complex creative processes. Filmmakers, designers and authors can take advantage of tools such as DALL-E to visualize artistic or scenic concepts, or text generators such as GPT to sketch scripts, musical pieces or articles.

These applications show how generative AI is not only a creative technology but also a strategic support for optimizing workflows, developing ideas, and reducing the burden of repetitive tasks. It is important to see it as an amplifier of human capabilities, allowing them to focus on the most strategic or value-adding aspects.

What are generative AIs: the most popular models and their characteristics

There are several generative AIs that have established themselves as leaders in the global technology landscape, each with specific characteristics and applications. These models, developed by leading technology companies and organizations, represent true benchmarks for innovative content creation.

• ChatGPT and GPT (OpenAI). Among the most celebrated natural language processing models, ChatGPT is based on GPT-3 and GPT-4 (Generative Pre-trained Transformer), models created by OpenAI. These tools are designed to generate natural text in responses to commands or questions, with applications ranging from article writing and programming to copywriting and customer experience support. Their ability to understand and produce contextualized content has made them major players in the generative AI boom. They are also behind SearchGPT, one of the first examples of an AI-based search engine.
• Gemini ( Google). It is an advanced generative AI model developed by Google to deal with complex queries and multimodal interactions. Based on the Gemini language model, this AI is designed to understand text, visual, audio and code input, responding with greater accuracy and flexibility than its predecessors. Released in beta phase in 2023 and officially renamed Gemini in February 2024, the system began as a direct competitor to OpenAI’s ChatGPT. With the launch of Gemini 2.0, Google introduced advanced reasoning capabilities, richer support for multimodal applications, and an increased focus on integration into tools such as Google Search and NotebookLM. Gemini does more than just answer questions-it can act as a personal assistant, exploring complex topics, compiling custom reports, and integrating with advanced systems to support developers and professionals in so many areas.
• Claude (Anthropic). One of the most innovative proposals, created by Anthropic, stands out for its focus on security and ease of use. It is designed to provide smooth interactions and enhance content creation through a user-friendly interface, limiting undesirable behavior through ethical design.
• Microsoft Copilot. Microsoft Copilot is an AI-based assistant designed to support users in a wide range of tasks, from word processing to visual and multimedia content generation. Integrated into platforms such as Microsoft Bing, Edge and applications on Windows 11, Copilot is based on the Prometheus language model , an extension of OpenAI’s GPT-4, enhanced by supervised and reinforcement learning techniques. Among its main functions are: creating content drafts, generating PowerPoint presentations, rewriting and optimizing text , and summarizing complex information. It can be used through specific commands, such as ! summarize (to summarize a text) or ! rewrite (to rewrite a text), often simplifying even articulate concepts for a non-expert audience. In addition to support in writing and content creation, Copilot relieves users of repetitive tasks in the work environment, enabling them to work more strategically and effectively. The multichannel nature of the tool-accessible on desktops, browsers, and even mobile devices-makes it a versatile choice for professionals and home users alike. However, as with other generative AI tools, it is essential to oversee the content produced by Copilot to ensure accuracy and originality.
• Stable Diffusion (Stability AI). A powerful artificial image generator. Stable Diffusion is based on diffusion models that transform textual descriptions into high-quality images. It is particularly used in art, design and advertising fields, offering accurate control over style and visual content.
• DALL-E (OpenAI). DALL-E represents another prominent development of OpenAI, specializing in creating images from textual descriptions. With the ability to combine elements in a unique and realistic way, it is highly valued for its use in creative projects, visual sets, and concept design prototyping.
• MidJourney. Popular among artists and designers, MidJourney focuses on producing artistic images in a style that ranges between the realistic and the surreal. It is a very versatile tool for those seeking imaginative AI with extraordinary visual impact.
• Grok (xAI). Created by the company xAI, part of Elon Musk’s constellation, Grok is a chatbot based on advanced language models designed to respond to textual queries and generate high-quality photorealistic images. Grok integrates directly with the social network X ( formerly Twitter), offering intuitive and free access, albeit with temporary usage limits (e.g., 10 prompts every two hours). One of its most innovative features is the generation of extremely realistic images, including believable portraits of public figures or images created from custom prompts. However, this feature has raised ethical concerns related to the possible dissemination of fake and potentially defamatory images.
• RunwayML. Designed for generating video and multimedia assets, RunwayML is distinguished by its multimodal (text, image, and video) creation. It is widely used in film, advertising and highly customized visual applications.
• AlphaFold (DeepMind). Although science-oriented, AlphaFold is an outstanding example of generative AI. It is used to predict protein structures, accelerating the drug discovery process and demarcating itself as a crucial tool for the medical research sector.

These models represent the spearheads of today’s generative technology, each designed for specific purposes but united by the ability to interpret and create original content. Choosing the best tool depends on your specific needs, whether you are writing an article or creating visual assets for a creative project, as also seen in our AI comparison test.

How to communicate with a generative AI: instructions and commands

The effectiveness of the communication and responses of a generative AI system relies on our ability to formulate an effective prompt. A prompt is nothing more than the command or request we give to the AI to guide its outputs.

A good relationship with these technologies is based not only on what we ask, but more importantly on how we ask it. The precision of instructions, context, and level of detail directly affect the quality of output, and here we try to find out what techniques are most effective for structuring prompts that are clear, targeted, and productive.

How to set up a perfect prompt

A perfect prompt is the result of a direct request that is easily understood by the AI. There are several approaches to communicating with a generative model, depending on the type of content we want to achieve. The main ones are:

• Direct Prompting. This is the simplest and most straightforward method, ideal for obtaining immediate responses. It consists of providing clear commands, without additional details or context. For example, “Create a 100-word text describing the benefits of a vegan diet” or “Summarize this article in three sentences”. This is a useful strategy when we know exactly what we want and the task at hand is circumscribed.
• Contextual Prompting. When the prompt is more complex, providing context and background is critical to guide content generation. The idea is to provide the AI with prior information that defines the content, tone, or context in which it will need to operate. For example, “Imagine you are an expert on environmental sustainability. Explain to a non-technical audience why recycling is important“ or ”You are a copywriter, create a catchy title for a blog about SEO”. With this approach we are creating a more defined framework that helps AI produce more relevant and consistent results.
• Iterative Prompting. This method takes a step-by-step, collaborative approach. Instead of requiring everything right away, we work in multiple steps, asking for progressive improvements to the generated output. The goal is to refine the content piece by piece. For example, we can start by asking, “Write a meta description for an article on SEO” and then continue, “Make the description more engaging and insert the phrase <optimize traffic>”. This technique is particularly useful for processing complex content or adapting the result to more specific needs.

Each method has different advantages and applications, but in each case clarity of the prompt is essential. Explaining exactly what kind of result is desired to the AI can make the difference between generic content and truly effective content.

The importance of providing context and detail

Not all prompts are the same, and not all AI responses automatically fit our goals. The richer and more specific the context provided, the more the output will conform to our expectations. It is therefore critical to construct prompts that do not just ask “what to do,” but provide guidance on how to do it, such as: tone of voice, format, or target audience.

If we want the output to have a professional, informal, or engaging tone, we need to specify it. For example:

• “Write a product description using a professional tone” .
• “Create a post for Instagram (max 100 words) using simple and engaging language” .

Similarly, providing detailed contexts is essential in the case of complex requests. Let’s think about a requirement in SEO: if we are creating an article, the prompt might contain not only the theme of the content, but also the keywords to be integrated, the desired number of words, or even the level of depth. An effective example might be:

“Create an 800-word article on SEO best practices, including the following keywords: organic traffic, SERPs, search engine optimization”.

Adding details allows the AI to better understand the context and tailor the response. This is especially useful for avoiding generic or off-topic content. Each piece added to the prompt helps to eliminate ambiguity and ensure a highly specific result.

Advanced prompting techniques

When you need to get more articulate content or are working on particular prompts, you can use some more advanced prompting techniques. These approaches are ideal for those seeking to take full advantage of AI’s flexibility in complex or creative scenarios.

• Role-playing prompting. With this technique, the AI is asked to play a specific role to provide output that simulates an expert’s expertise or empathy toward a given context. For example, “You are a Renaissance historian. Explain the importance of Leonardo da Vinci in the culture of the time“ or ”Act as a business consultant: propose a strategic plan to reduce operating costs”. This method allows for outputs that have a tone and style consistent with the required figure, adding credibility and accuracy to the generated content.
• Recursive prompting. Also known as “recursive prompting,” this technique is based on iterative requests for revision and improvement. Instead of just the initial output, the AI is asked to self-evaluate or refine its response. For example, “The answer you provided is not persuasive enough. Can you rephrase it by making it more punchy?“ or ”Make the content clearer for a non-expert audience”. Recursive prompting fosters a collaboration between user and artificial intelligence, pushing the model to generate increasingly accurate and adherent outputs.

These more advanced techniques not only improve results, but also stimulate AI creativity, creating content that is personalized, targeted and fit for specific purposes. Knowing how to leverage these methods allows one to overcome the limitations of simpler requests and take the relationship with AI to the next level.

The evolution and context of generative AI

The idea ofartificial intelligence capable of creating new content is not a recent one. However, the path that led to the revolutionary tools we know today, such as ChatGPT or Gemini, is based on years of theoretical, technological and application advances. Starting with the earliest artificial intelligence models and continuing with decisive developments such as transformers and Large Language Models (LLMs), we can trace how we arrived at the multimodal technologies that are transforming entire sectors, from scientific research to customer service, from medicine to creative design.

• From theoretical beginnings to their application on a global scale

In the 1950s, Alan Turing envisioned a machine capable of interacting with humans through natural language (later referred to by the famous “Turing Test”), but it was not until the 1980s that research on artificial intelligence models began to gain traction. These were then rudimentary systems, such as autoencoders, designed to reduce and reconstruct information, or the first supervised machine learning approaches .

The real breakthrough came in the early 2000s with the consolidation of deep learning, which enabled deep neural networks to process data with unprecedented efficiency. These algorithms were initially applied in specific domains, such as image recognition or machine translation (emblematic example: Google Translate), but the first creative possibilities of AI were already being glimpsed.

• The crucial role of transformers and advanced language models

In 2017, Google Research’s paper “Attention is All You Need” introduced the world to the transformer architecture , which was created to address the limitations of the then-dominant recurrent neural networks (RNNs). Transformers enabled AI to process entire sequences of data simultaneously, introducing the concept of self-attention, which analyzes relationships between words or elements in a broad context, regardless of their location.

This was the moment when artificial intelligence really began to become “generative.” From that model derive:

• Google’s BERT (Bidirectional Encoder Representations from Transformers) (officially unveiled in 2019), which revolutionized search engines’ understanding of semantic contexts by improving the processing of complex queries.
• GPT (Generative Pre-trained Transformer) by OpenAI (2018), which led to the creation of models capable of generating entire paragraphs of coherent text.
• Google’s MUM (Multitask Unified Model) (2021), capable of processing multimodal inputs (text, images, etc.) and responding to complex queries, fostering advances in global information processing.

For many years, these developments remained confined to the realm of academic research or highly specialized fields. It was only with GPT-2 (2019) and later GPT-3 (2020) that generative AI became accessible to the public. The ability to produce human language-like output, coupled with the support of user-friendly interfaces, paved the way for commercial and creative use for a wide range of users, from writing professionals to software development teams.

• The evolution to multimodal models and large-scale applications

Today, generative artificial intelligence is no longer limited to text: it has become multimodal, capable of handling different types of data simultaneously. The integration of textual and visual inputs in models such as GPT-4, Gemini , and Grok enables the creation of content ranging from videos to music tracks to scientific simulations. This shift has transformed generative AI into an enabling technology for a surprising variety of industries.

Some key applications include:

• Medicine and chemistry: Tools such as AlphaFold (DeepMind) have used generative AI to predict protein structure, accelerating drug discovery and significantly reducing research costs.
• Research and journalism: models such as MUM enable real-time aggregation of information from different languages and sources, helping journalists and researchers interpret complex data-although the relationship between AI and Italian newsrooms is still stormy…
• Design and creativity: tools such as DALL-E , Stable Diffusion and MidJourney generate tailored images, offering innovative solutions for branding, advertising and game design.
• E-commerce and personalization: generative AI is used to create product descriptions at scale, optimize cross-selling strategies, and even generate realistic images of items for display in digital catalogs.

The impact of Google and integration into search systems.

Google has played a key role in the mainstream adoption of generative AI, integrating it directly into its own search systems and AI platforms. Although not everyone knows it, in fact, the Mountain View giant’s journey in integrating AI systems into Search began well before generative AI became a global phenomenon and originated with RankBrain, in 2015, and then moved through milestones such as Neural Matching, BERT, and MUM, to the introduction of Gemini, Google’s latest generation of multimodal AI. Each of these steps has enabled the search engine to significantly improve both the understanding of queries and the quality of answers offered to users.

In the past, before the use of artificial intelligence, Google’s search systems based results predominantly on exact matches of keywords. This approach, although functional, often failed to respond effectively to more complex or misspelled queries. With the introduction of RankBrain, Google revolutionized the way queries were handled: for the first time it was possible to understand and interpret the underlying concepts of words, linking them to real world meanings. For example, the search engine became able to identify that an ambiguous query such as “consumer title at the top level of a food chain” referred to the “apex predator” in natural ecosystems.

With the launch of Neural Matching, tested in 2018 and then fully integrated into search systems, Google paved the way for an even more sophisticated understanding of the relationships between queries and content. This system did not just compare individual words, but analyzed underlying concepts, “connecting the dots” even when the language used is less direct. The algorithm was refined further with the arrival of Bidirectional Encoder Representations from Transformers in 2019: BERT represented a huge step forward in understanding natural language, enabling Google to capture semantic nuances within queries. This technology is remarkably effective at considering the broader context, even from seemingly irrelevant words. For example, in the query “can you pick up medicine for someone pharmacy”, BERT is able to discern that the user wants to know if they can pick up a medication for another person, a concept often overlooked by previous algorithms.

However, the real breakthrough came with the introduction of Multitask Unified Model during Google I/O in 2021. MUM is thousands of times more powerful than BERT and is designed to understand and generate content in a multimodal way, interpreting queries that combine text, images, and audio information. Although it is not currently used as a result ranking system, it is already active in specific functions, such as support for COVID-19 vaccine information and related results in videos. MUM represents Google’s vision toward a search based not only on natural language, but also on a more nuanced and comprehensive understanding of information.

The latest step in this evolution is the launch of Gemini, the multimodal language model that replaced (not only nominally) Bard in 2024. With Gemini, Google is completely reorganizing the way users interact with search. This model is designed to offer an integrated response between different types of input, improving the type of information retrieved, synthesized, and presented to users. For example, Gemini quickly identifies relevant results and is also able to generate original content that articulately responds to particularly complex queries.

One of the most innovative applications of Gemini and generative AI in search systems is AI Overviews, currently available only in certain regions of the world-excluding Europe. This feature uses artificial intelligence to generate comprehensive summaries of information found online, presenting it in the form of “visual summaries” that offer users an immediate and personalized response. AI summaries do not replace traditional links, but complement them by including external references and resources that allow the user to delve deeper into the topic on their own. For example, searching for a complex query on a scientific problem or topical issue, the user might find, above the classic results, a generative summary combining several reliable sources, accompanied by related links.

AI Summaries also revolutionize the way information is displayed and organized in search results. By using templates such as Gemini to synthesize input from the most authoritative and up-to-date sources, Google is able to offer answers that better reflect the context and specific demands of each query. This feature has already been particularly popular for the time savings it offers, allowing users to find clear and complete answers with fewer searches.

All of these Google AI systems-RankBrain, Neural Matching, BERT, MUM, and Gemini-operate in synergy, working on complementary aspects of language and information understanding. They never act in isolation, but collaborate to provide an optimized search experience, reducing ambiguity, improving accuracy, and bringing Search closer to the real language and needs of users.

Google’s impact in the evolution of search is evident not only in the quality of results, but also in the continuous improvement of experiences related to the semantic understanding of queries and the transformation of SERPs into smarter and more useful tools for all kinds of users.

Practical applications of generative AI

We see it every day: tools powered by artificial intelligence are now an integral part of the toolbox of those working in the digital sphere, especially in the field of writing, where it has brought efficiency and creativity to areas such as digital marketing and SEO. Indeed, its ability to produce text, images and even multimedia content in a matter of moments makes it a strategic ally for companies, professionals and creators.

One of the most relevant applications of generative AI is the creation of content optimized for search engines, because it can analyze and incorporate specific keywords, making texts more effective in attracting organic traffic. In addition, it allows generating catchy titles, optimized meta descriptions and structured content to increase CTR (Click-Through Rate), and most importantly it reduces search and writing time, simplifying processes for copywriters and SEO specialists.

We have a concrete example of this in SEOZoom, with our AI-based tools that combine generative algorithms with proprietary data on user search intent and competitive analysis, for example, to support the creation of texts optimized for specific needs.

But the application of generative AI in SEO is not limited to article production, proving valuable for more specific tasks as well, such as creating FAQs based on users’ frequently asked questions, identifying long-tail keywords to attract highly segmented audiences, ChatGPT-assisted editorial plans, or developing custom landing pages for PPC campaigns.

The main advantage is that all of this is done in a streamlined way, pairing human creativity with the technological efficiency of AI. The goal is not to replace the copywriter’s role, but to enhance it, allowing more attention to be devoted to strategies and content optimization.

Where to use AI: visual and multimedia content creation

Generative AI has also reached impressive levels in the visual and multimedia sector , offering innovative solutions for social campaigns, e-commerce sites and creative projects. Some key tools allow simple text descriptions to be transformed into images, video or audio with extreme customization.

Some of the most energetic applications include:

• Text-based image generation: tools such as DALL-E, MidJourney and Stable Diffusion allow you to create photorealistic or artistic images from accurate descriptions. This is especially useful for advertising campaigns, where each visual element must be aligned with the brand message. For example, an agency might use DALL-E to generate visual concepts for a series of ads based on seasonal themes (such as Christmas or summer).
• Custom video creation: Tools such as RunwayML and DeepBrain enable the generation of videos from text scripts. Social campaigns in which short explanatory or promotional videos are created entirely by artificial intelligence are now common, saving hours of manual labor.

Generative AI applied to e-Commerce has revolutionized image creation for product catalogs. Thanks to models such as Stable Diffusion or DALL-E, it is possible, among other things, to create photographic variants of a product (e.g., alternative colors, different settings) without the need for costly shoots, or to generate high-resolution images perfectly optimized for inclusion in marketplaces such as Amazon or proprietary sites.

Another innovative example is MusicLM, an artificial intelligence model developed to generate music tracks from textual input. This tool is proving useful for personalizing audio experiences, from YouTube videos to interactive installations in physical stores.

In digital marketing, the integration of visual and multimedia content creates emotional impact and connects audiences on a deeper level. In social campaigns, for example, personalized videos and images manage to improve engagement, increasing the likelihood of interactions and conversions.

Beyond digital: applications of generative AI in professional settings

The possibilities offered by generative AI extend far beyond the field of SEO and digital marketing, spanning a wide range of industries and contexts. This technology has proven to be a powerful tool not only for optimizing content for the Web, but also for revolutionizing sectors such as medicine, publishing, the creative industry and scientific research.

One of the most relevant examples is generative AI applied to healthcare, where tools such as AlphaFold are able to predict the structure of proteins, a crucial element in accelerating drug discovery and improving treatments. Similarly, in medical imaging, generative models use synthetic data to train advanced recognition systems, reducing gaps in sensitive datasets and increasing accuracy.

In publishing and creative production, generative AI opens up new perspectives. Authors and screenwriters can use models such as GPT to co-create plots or develop custom scripts, saving time in ideation processes. At the same time, tools such as DALL-E and MidJourney are emerging in the fields of art and design, generating stunningly realistic or artistic images from simple text descriptions, revolutionizing the creative process for graphic designers, game designers and filmmakers.

In the financial services sector , banks and institutions are using generative AI to speed understanding of complex data and generate customized reports for clients. Chatbots based on generative models improve the experience of interactions by assisting customers with quick and contextualized responses, integrating personalized analytics based on user history.

Even in the legal and bureaucratic domain , generative AI is changing the rules of the game. Generative synthesis systems are able to analyze contracts, extrapolate key clauses and formulate draft legal documents, simplifying processes that normally require hours of manual labor.

Another revolution taking place is in education, where generative models can personalize learning, creating learning materials suited to the student’s level or detailed summaries of complex topics. At the same time, universities and training centers are using multimodal models to produce interactive simulations or innovative educational content.

These examples represent just the tip of an iceberg: generative AI is becoming a major player in a new technological era, with ever-expanding possibilities. Its versatile nature allows it to adapt to any field that requires innovation, automation and optimization, representing a platform for transformative solutions in a wide variety of fields.

SEOZoom’s generative AI tools

We mentioned them earlier, and we can’t help but devote a little focus to our advanced tools integrated into SEOZoom, which harness the power of artificial intelligence to generate and optimize content, yet stand out through the use of unique SEO data, which ensures targeted outputs tailored to users’ specific needs.

In addition, the availability of already optimized and ready-to-use prompts eliminates uncertainties in initial setup, making the process easier and more straightforward even for those unfamiliar with generative models.

Our AI-based toolbox includes:

1. AI Writer for SEO-oriented texts

AI Writer is the tool that writes like a copywriter and thinks like an SEO specialist. It takes AI-assisted writing to the next level by combining SEO data from SEOZoom-including keywords, competitor rankings, and search trend analysis-with automatic generation of high-quality text to enable the creation of SEO-optimized content quickly and strategically.

Key benefits include predefined prompt templates based on SEO best practices, which avoids the need to create AI prompts from scratch. The user only needs to choose a target keyword and set a few parameters to get a structured article already designed to climb Google results. In addition, the desired characteristics for the text can be defined, choosing the tone of voice (e.g., formal, colloquial, or technical), the optimal length, and even the type of author imagined, such as an industry specialist or blog content creator. The AI Writer uses data about the best content placed in SERPs to create articles that exactly match users’ search intent, maximizing content relevance.

In this way, those working on creating an article on a specific topic can select frequently asked questions related to the keyword or analyze competitor headings to automatically define the structure of the content. This ability to combine artificial intelligence and SEO strategy results in articles that are not only well-written, but also highly performing.

2. AI Assistant for content review

Another central feature is AI Assistant, designed to assist users during content review and ensure that each text meets the highest standards of quality, readability, and “human” empathy.

It works by analyzing text to detect readability issues, misalignments with search intent, and opportunities for SEO improvement. The next step is the reporting of specific suggestions, such as adjusting sentence length, optimizing keyword usage or improving cohesion between paragraphs, which is completed by detecting any stylistic problems or discrepancies in the tone of the text, making the content not only technically optimized but also more engaging for users – and, as Ivano demonstrated, this also translates into the preference of AI algorithmic systems!

Thanks to the contextual prompts already provided, the assistant can be activated with just a few clicks, without the need for complex configurations. This further simplifies the review of multilingual content or content intended for international audiences, helping professionals maintain uniformity and effectiveness at a large scale.

3. Generative Fill

Generative Fill is a versatile solution for anyone who needs to edit, expand or optimize content directly within the SEOZoom editor, without the need for external applications. This tool integrates generative AI into daily work, improving responsiveness and productivity during editing. With a few clicks, we can select an existing section and ask the AI to expand it, adding details, examples or explanations, or to reword it to make it more punchy and consistent with the overall tone. Or, with the help of preset prompts, the AI quickly produces new variants of optimized headlines or improved paragraphs, adapting to the context of the page being worked on. Still, by entering a specific keyword, generative filler rephrases or supplements the content to align it with relevant queries or emerging search intent-for example, when writing a landing page, you can prompt the AI to complete a product description or create a more engaging call-to-action, all without leaving the editor interface.

4. AI tools for maximum productivity

The offering is rounded out with the 37 verticalized AI Tools, designed to cover every need related to content creation and optimization. Each tool has preconfigured prompts, designed to ensure specific results with minimal input, saving time and effort.

In particular, there are tools to create targeted posts for Facebook, LinkedIn, Instagram, X and Threads, with copy optimized to engage users of the chosen platform. With preset templates, simply provide a basic message and AI generates a stylistically and formally perfect version for the selected channel. For those working in e-Commerce, the generation of product sheets, long descriptions or buyer personas is supported by dedicated tools, which are also capable of batch working to create entire catalogs in a few steps by simply uploading a CSV file.

With these solutions, SEOZoom does not just provide generative technology: it integrates strategic and operational practices that enable marketers, copywriters and SEO specialists to maximize their productivity and optimize content intelligently.

Opportunities and limitations of generative AI

Generative AI represents an unprecedented opportunity to transform the way we create content, process ideas, and optimize workflows in various professional domains. However, as with any revolutionary technology, its potential coexists with technical limitations and ethical issues that require attention and awareness. In short, alongside the strategic advantages we have mentioned we need to list (and reflect on!) the critical issues to keep in check, so that we have a balanced view of what generative AI can really offer.

The opportunities to exploit

To recap, generative AI offers powerful tools to streamline processes, reduce repetitive workloads, and expand creative possibilities. Its main benefits include:

• Workflow optimization. Automation of repetitive tasks represents one of the most obvious revolutions. Generating fully customized articles, product descriptions, social media copy, or even visuals with just a few inputs allows professionals to focus on more strategic tasks, such as devising complex campaigns or analyzing data. For example, with our AI Writer an entire SEO article can be generated in minutes, already structured according to search engine requirements.
• Speeding up production. Where previously hours of work were needed, generative AI dramatically reduces the time needed to produce text, images, and video. This advantage is particularly useful in high-pressure areas, such as newsrooms, e-commerce or advertising agencies, where speed is a crucial competitive factor. Notable applications include image generation systems such as DALL-E and Stable Diffusion, which enable the creation of unique visual assets for advertising campaigns and marketing materials.
• Augmented Creativity. Generative AI not only automates tasks, but also acts as a catalyst for innovative ideas. It is able to come up with solutions and combinations that might not be immediately thought of by a human mind. An example is the music industry, where tools such as MusicLM produce soundtracks from a few textual descriptions, or art design, where generative models such as MidJourney offer extraordinary visual interpretations from simple inputs.
• Tailored customization. The ability to integrate specific inputs makes it possible to produce content with a high degree of personalization: copy optimized for target audiences, images tailored to different social channels, or contextualized chat responses based on user preferences. In areas such as customer service, chatbots such as ChatGPT and Grok succeed in enhancing the customer experience by proposing precise and engaging solutions.

These opportunities enable professionals to no longer limit themselves to manual work, but to use generative AI as a tool to maximize productivity, quality, and scalability.

The limits to be kept in check

But let’s come to the other side of the coin, the “darker” side, namely the inherent limitations of generative AI, both from a technical and ethical point of view , which can undermine the effectiveness of work and expose to real risks-without going into the concerns the professions at risk because of AI!

• Hallucinations (hallucinations). One of the most discussed problems is the phenomenon of “hallucinations”, that is, when a generative AI produces information that seems plausible but is totally incorrect or invented. This happens because the models are based on statistical correlations rather than a real understanding of the meaning or truthfulness of the content. As Giuseppe Liguori points out, a language model such as ChatGPT can return inaccurate answers if the prompt is vague or if the context is not clarified sufficiently by user input. These errors make human oversight essential, especially in fields such as journalism or medicine, where accuracy is a requirement. For example, an AI might generate an article that incorrectly states that a historical figure performed an action that never occurred, simply because the training data were incomplete or ambiguous. This underscores the importance of validating AI-generated information before using it.
• Deepfakes and risks of manipulation. Generative AI is not limited to text, but can create extremely realistic images and videos, sometimes indistinguishable from reality. This leads to the problem of deepfakes, manipulated content that can be used to spread misinformation, compromise people’s reputations, or even defraud entities in sensitive areas. A prime example is given by Grok, xAI’s chatbot, which is capable of generating images of famous people that can be used for controversial purposes, such as fake portraits in offensive situations. The danger is amplified by the ease with which this content can be shared and make it difficult to distinguish real from fake.
• Dependence on the quality of training data. The effectiveness of generative AI is strictly dependent on the quality and diversity of the data with which it is trained. If the datasets are limited, distorted or outdated, the model is likely to produce inaccurate or biased content. For example, a visual generator such as Stable Diffusion might create inconsistent images if its datasets do not correctly represent certain realities or styles.
• Issues of copyright and originality. Generative models learn from existing datasets, which can lead to challenges vis-à-vis copyright. An AI-generated image might pick up stylistic elements or specific details found in training data, raising questions about its actual originality and legality.
• Ethics and social responsibility. The unchecked use of AI tools raises fundamental questions about the role and responsibility of users. AI’s ability to generate content quickly can be exploited with malicious intent, from fake news creation to political manipulation. Thus, a regulated approach that balances innovation and ethics becomes essential.

In summary,generative AI offers tremendous opportunities, but its adoption requires caution and awareness. The added value of this technology is revealed when it is used responsibly, with human oversight capable of avoiding mistakes, managing risks and ensuring that the content produced is ethical, accurate and of quality. Only then can we exploit its full potential without stumbling over its limitations.

How to make the most of generative AI

But how can we get the most out of generative AI tools? Obviously, it is not enough to think of totally relying on a tool to automate processes, but rather we need a strategic approach that combines machine efficiency with human creativity. The key to optimal results lies in the ability to control and target AI tools, exploiting their potential without losing sight of the uniqueness and value of human expertise.

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We came up with these practical suggestions:

1. Define clear and specific goals

Before using an AI-based tool, it is essential to be clear about what we want to achieve. The output provided by AI will be the more relevant and satisfying the more detailed our input. This means not only stating what we want (text, images, etc.), but also defining:

• Tone of voice – formal, technical, colloquial or other.
• Target audience – e.g., expert audience, potential clients, readers of a blog.
• Desired format – a long article, a meta description, a short social post.

An effective example of a well-worded prompt might be, “Create an 800-word article that delves into the benefits of local SEO. Use a professional tone and include the following keywords: local SEO, online visibility, SMB strategy.”

Clearly defining goals reduces the need for multiple revisions, optimizing time and effort.

2. Use AI as a support, not a replacement

Generative AI should never be considered a substitute for human expertise, but rather a strategic support. This means that AI-generated content can be a strong foundation that, however, requires human oversight to ensure quality and accuracy.

For example, for SEO-oriented texts, the AI output might already be optimized for keywords and structure, but human editing is used to enhance style and insert custom or contextual elements. In contrast, for visual or creative presentations tools such as DALL-E or MidJourney can generate visual drafts that will then be refined by human designers, maintaining consistency with brand identity.

AI generates output extremely quickly, but it is up to the human eye to turn it into valuable content.

3. Take advantage of preset prompts

Many advanced tools, such as those found in SEOZoom, offer preset and optimized prompts, which make them easy to use even for those unfamiliar with generative templates. Instead of creating each prompt from scratch, you can start from ready-made templates to address specific needs. Using these prompts reduces not only the margin for error, but also the time it takes to become familiar with the tool and focus on improvements.

4. Refine output with iterative prompting

The first response generated by AI does not always fully meet expectations. Therefore, it is useful to adopt an iterative prompting strategy, which consists of providing feedback to the model to progressively refine the output. For example, we can initially ask, “Create a product description for this item.”; later, request, “Make the description more persuasive and include a call-to-action.” This approach allows us to collaborate with the AI model, increasing the accuracy and usefulness of the output without extensive manual revisions.

5. Combine AI with analytics and data

Integrating generative AI with advanced analytics and real data is essential to achieving strategic results. In SEOZoom , this is done automatically to align texts with the best SEO standards, search trends, or competitor performance. For example, with AI Writer we can draft articles that incorporate performing keywords and respond to users’ search intent, thus optimizing ranking in SERPs. But visual assistance, such as RunwayML, also allows us to develop multimedia content based on previous engagement analytics. The synergy between data and AI increases the overall effectiveness of content.

6. Always monitor content accuracy

Although generative AI is powerful, it is not error-free. Phenomena such as hallucinations can lead AI to generate information that is demonstrably false or inaccurate. For this reason, it is crucial to validate the data provided by AI, especially in technical or scientific articles, and check that the output respects the context, avoiding errors that could undermine the credibility of the content. For example, an AI might unintentionally use outdated data in the case of statistics. Human supervision is therefore indispensable.

7. Using AI to experiment with new ideas

Generative AI is an excellent engine for brainstorming. It can be used to generate innovative ideas for articles, headlines or advertising campaigns, or even to come up with creative alternatives, such as visual concepts or naming for products. For example, a prompt such as, “Suggest five headlines for an article on digital transformation in SMEs” can provide valuable insights that would otherwise require hours of manual brainstorming.

8. Maintain an ethical and conscious approach

Last but not least, it is essential to use generative AI ethically and transparently. This means being aware of risks (e.g., avoiding spreading false information or using copyrighted content created by AI), and recognizing the human role in reviewing and approving generated content.

A responsible approach not only protects against legal issues, but also enhances credibility and trust between consumers and the brand.

Generative AI: FAQs and frequently asked questions

Generative AI is proving to be an extraordinary technology, capable of transforming the way we manage content creation, complex problem solving, and even the optimization of creative and production processes. However, as we have seen in this article, its use is not without questions, doubts and challenges that require attention and awareness.

For this reason, we have collected some of the most frequently asked questions that arise when discussing generative AI, with the aim of providing clear and practical answers. After all, understanding how it works and how to make the most of it is the first step to integrating it intelligently and strategically into your daily work routine.

• What is a generative AI model?

A generative AI model is a system that uses artificial intelligence algorithms to create new content from user-supplied input, such as text, images, or sound. It is “generative” because it does not just analyze, but invents original outputs based on patterns learned from the data it has been trained on.

• When did generative artificial intelligence originate?

The concept of generative artificial intelligence began to take shape in the 1980s with the first neural network models capable of reproducing simple data structures, but it is only in the last decade that this technology has made significant strides. A key moment was 2014, when Ian Goodfellow and his team introduced GANs (Generative Adversarial Networks), which transformed the way neural networks create new content. However, the widespread adoption of generative artificial intelligence has mainly occurred since 2020, with the introduction of tools such as OpenAI’s GPT-3, which have made some of its more advanced applications accessible, pushing it to the center of digital innovation.

• How does a generative model work?

A generative model works through a sophisticated mathematical process that combines machine learning and neural networks. During the training phase, the model is exposed to huge amounts of data (e.g., text, images or video) to “learn” the relationships that exist between its constituent elements. This knowledge is stored in the form of parameters that the model uses to generate new content. The fundamental principle is probabilistic prediction: the model calculates which element (a word, a pixel, a musical note) is most likely to follow in a sequence, thus going on to construct result consistent with the provided context. For example, if we provide a prompt such as “Create an article on SEO,” the generative model will analyze the words, their relationships, and context to create text that is relevant. Tools such as GPT or DALL-E are concrete examples of this technology.

• What is a Large Language Model (LLM)?

An LLM is a type of generative AI model trained on huge amounts of textual data to understand and generate natural language. Using technologies such as transformers and word embedding, an LLM analyzes context and predicts the next word in a sequence, building coherent and contextualized responses.

• What is meant by machine learning?

Machine learning is a branch of artificial intelligence that allows computers to learn and improve autonomously through data analysis, without having to be explicitly programmed for each task. In practice, machine learning models identify patterns and rules within huge datasets, which are then used to make predictions or make intelligent decisions. For example, a machine learning model can be trained to identify objects in an image, translate a language or suggest relevant content to users. It is the foundation on which many AI applications are based including generative AI, which leverages data and these patterns to create new and original content.

• What is a GAN (Generative Adversarial Networks)?

GANs, or Generative Adversarial Networks , are a type of neural network introduced in 2014 by Ian Goodfellow. It is an innovative architecture consisting of two neural networks working in competition: one called the generator and the other the discriminator. The generator creates new content (such as images or text), while the discriminator evaluates this content by trying to distinguish between artificially generated content and real content. This competitive process leads the generator to continuously improve the quality of its outputs, until it produces outputs so realistic that they are indistinguishable from authentic ones. GANs are used in various fields, such as photorealistic image generation, deepfake creation, or synthetic data simulation, and represent one of the most important developments in generative AI.

• What are the main limitations of generative AI vis-à-vis human creativity?

Despite its ability to generate creative content, AI relies solely on the data it has been trained with and the input it has provided. It does not have the ability to intuit, sense emotions or innovate in an original and completely independent way.

• Does AI really understand what it does?

No. Although the results may seem intelligent and contextualized, generative AI models do not possess true awareness or understanding. They rely solely on mathematical connections and statistics.

• How does generative AI differ from traditional models?

Traditional models (called discriminative) analyze data to classify or predict an already known outcome, while generative models create something entirely new , leveraging knowledge learned from pre-existing data.

• How is generative AI trained?

Generative AI training is based on a preliminary phase in which the model is exposed to vast datasets containing structured and unstructured data, such as text, images, audio or video. This data is used to teach the model to recognize patterns and relationships among information so that it can “predict” and generate new coherent content.

• Data sources: where do they come from?

The data used to train a generative AI system come from a variety of sources. Those of text type, for example, involve encyclopedic articles, digitized books, websites, blog articles, social media posts, forum discussions, academic papers, and reports. For images, visual datasets collected from the Internet (e.g., datasets such as ImageNet or LAION) and from open-source image archives. Again, audio and video are based on publicly accessible audiovisual resources, such as podcasts, educational videos, or open-source libraries. The criterion behind the process is that the dataset used is large and varied, to ensure the model’s ability to generalize and produce relevant content in different contexts. For Large Language Models (LLMs), such as GPT or Claude, the corpus of textual data can include up to hundreds of billions of tokens (language units, such as words or text fragments).

• Who selects the data?

Normally, data selection is the responsibility of the AI development team, which seeks to include reliable, ethical, and representative sources. However, this selection is not perfect and may include errors or inconsistencies. Some data are filtered to remove irrelevant content, such as spam, hate speech, or unsafe information. Despite this cleaning step, it is not always possible to remove everything that might compromise the accuracy or ethicality of the model. Often, teams rely on existing, publicly available datasets, but decisions about which ones to include or exclude are critical to the quality of the training.

• Why can errors arise from datasets?

There are several variables that can cause errors in the outputs. Perhaps the main limitation is obsolete temporal data, because AI models are “trained” on data available up to a certain date (e.g., GPT-4 was trained on data up to 2023). This means that they are often unaware of recent events, developments, or subsequent updates. For example, an article on an emerging technology might be dated or fail to include later advances.

In addition, there may be biases inherent in the data, introduced natively by the datasets themselves, such as cultural bias (if a significant portion of the data comes from sources that reflect only a dominant culture),gender and social bias (if the data include gender, ethnic or socioeconomic stereotypes, these may be replicated by the model),language or regional bias (models tend to be more accurate in languages or regions that dominate their datasets-such as English than in less represented languages).

Other possible biases arise from noisy or unverified data, because datasets may include incorrect, incomplete, or unverifiable information, especially when derived from unchecked public sources, such as forums or social media. And these “noises” can generate inaccurate or misleading responses.

Finally, lack of context is also “risky” : models do not “understand” data as a human would and cannot distinguish between objective facts, personal opinions, or ironic content, leading to significant errors.

• What are AI hallucinations and how to avoid them?

AI “hallucinations” are errors in which the algorithm generates seemingly plausible, but completely incorrect or baseless answers. To avoid them, it is important to structure detailed prompts and always verify the generated results by comparing data with reliable sources.

• Does generative AI only work online?

Many AI tools require Internet connection for access to cloud-based models, but there are also offline or on-premise solutions for companies that need more control over their data or seek to integrate pre-trained models locally.

• What skills are needed to work effectively with generative AI?

Advanced skills are not required, but understanding natural language to write effective prompts is critical. Familiarity with the platforms used (e.g., SEOZoom or DALL-E) certainly helps. For more technical areas, knowledge of programming basics or data analysis can be helpful.

• In what areas does generative AI find the most widespread applications?

Generative AI is widely used in SEO and digital marketing, but it also has significant applications in creative production (images, music, video), medicine (protein modeling with AlphaFold), scientific research, customer service (chatbots such as Grok and Copilot), and automated writing of technical documentation.

• Can AI be used to do development and coding?

Yes, AI can already support developers and programmers in a variety of ways, improving efficiency and productivity. Tools such as GitHub Copilot, based on Codex (an extension of GPT), can generate code snippets, suggest solutions, and even complete entire scripts from descriptive queries or functions already written. It can also be useful for debugging, translating from one programming language to another, or providing automated documentation. However, AI does not replace the work of developers: its role is to accelerate and optimize repetitive or less critical tasks, while human supervision remains crucial to assess the correctness and efficiency of written code.

• Can generative AI support learning and training?

Absolutely. Models such as ChatGPT or Copilot can answer complex questions, provide detailed explanations or create customized teaching materials, making them excellent tools for teachers and students. They can also be used for interactive simulations or to develop dynamic learning experiences.

• Can generative AI help improve collaboration in teams?

Yes, it can be used to centralize and optimize workflow-from assisted brainstorming to generating reports and meeting summaries to creating shared materials for presentations or collaborative projects.

• How can generative AI be used for social media marketing?

Generative AI is ideal for creating posts optimized in terms of format, tone and length for platforms such as Instagram, X (Twitter), Facebook and LinkedIn. It can generate persuasive copy, suggest hashtags and develop campaigns consistent with brand identity.

• How can I generate consistent images for social with AI?

Tools such as DALL-E, MidJourney or Stable Diffusion turn textual input into customized images. To ensure consistent results, it is important to provide detailed prompts, including desired style, colors, and format (e.g., “Create a minimalist image of a cityscape at sunset”).

• Are AI-generated images and content protected by copyright?

It depends on the platforms used and local laws. In general, AI-generated content can be used freely by the user, but some tools specify that the rights of use belong to the AI provider (e.g., OpenAI, Microsoft). It is always good to check the terms of service.

• Are there security risks in using AI tools?

Yes. Providing confidential or sensitive information in prompts can pose a risk. AI models temporarily store processed data and, even if well designed, could be subject to security breaches or manipulation. Therefore, you should always use secure tools and protect sensitive data.

• What are the best practices to protect data while using AI?

Use trusted platforms that respect privacy. Avoid sharing sensitive or confidential information in prompts. Verify that the tool used complies with data protection regulations (e.g., GDPR in Europe).

• How to verify the accuracy of AI-generated content?

To verify the accuracy of the output, it is necessary to compare the data provided with authoritative sources and use fact-checking tools for scientific or technical content. In addition, you need to make sure that the context is consistent with the content’s objective.

What is a prompt?

A prompt is the input provided to a generative AI model to tell it what to generate or what topic to work on. It is the “prompt” that guides the AI’s creative process. For example, for a text generator such as GPT, a prompt might be a sentence or question, such as “Write an article on environmental sustainability”. For an image generator such as DALL-E, on the other hand, a prompt might describe a scene, such as “A cat reading a book under a tree at sunset”. The quality and clarity of the prompt directly influence the relevance and accuracy of the AI-generated result. The more specific and well-worded the prompt is, the more likely it is to produce a satisfactory output.

• How do you write a good prompt?

A good prompt should be clear, specific, and detailed. Including the desired tone, output format, and context is critical. For example, instead of “Write a post on digital marketing” , you can write, “Create a 100-word LinkedIn post with professional tone explaining 3 benefits of generative AI for marketing.”

• What AI tools are suitable for SEO?

More advanced tools for SEO include SEOZoom, with features such as AI Writer for generating optimized articles and AI Assistant for SEO-friendly reviews. Other useful templates include Jasper AI or Copy.ai, but it is the combination with accurate SEO data that really makes the difference.

• What are the risks of publishing texts without human review?

Publishing AI-generated texts without supervision is risky. There could be conceptual errors (the “hallucination” phenomenon), outdated information, or style choices that are inconsistent with the tone of the brand. Human review is essential to ensure accuracy, quality and relevance of the content.

• Can generative AI improve product sheet copy?

Absolutely. SEOZoom ‘s AI tools make it possible to generate search engine-optimized product descriptions, emphasizing key points through well-chosen keywords. In addition, AIs such as Jasper or Writesonic can refine language to make text more persuasive and engaging.

• Is AI-generated content detectable by Google and penalizable?

Google does not automatically penalize AI-generated content. However, the focus remains on quality and value to the user. Low-quality or copied content, even if generated by AI, can still be downgraded.

• How much time is saved by using SEOZoom for AI content?

SEOZoom greatly speeds up the content creation and review process. Thanks to tools such as AI Writer and AI Assistant , what would require hours of manual work can be completed in a matter of minutes. This allows professionals to spend more time on strategies and less on operations.

• What is the average cost of advanced AI tools?

Costs depend on the tool and the pricing model. In SEOZoom we have implemented a pay-as-you-go system (extra subscription) that allows for very affordable costs (a few cents per item). Others, such as Jasper AI, use monthly subscriptions that start at around 50-100 euros.

• What is the maximum limit of content I can generate with an AI tool?

The limit varies depending on the tool. For example, SEOZoom uses a consumption-based system with credits; other tools, such as Jasper AI, have monthly limits based on the subscription chosen. Free tools may have stricter limitations, such as a maximum number of tokens or daily requests.

• Can I customize AI models for specific business needs?

In many cases, yes. Some advanced tools, such as those offered by OpenAI or Hugging Face, allow fine-tuning of models to fit a particular domain or industry. This often requires advanced technological skills or support from a technical team.

• Why is it important to perform human control and review of outputs?

The limitations of datasets underscore the crucial importance of human oversight. AI outputs must always be verified, especially in sensitive contexts such as news, science, or technical documentation. It is then the responsibility of developers not only to remove bias from training data, but also to instruct users on how to interpret and use generative AI outputs. Understanding the basics of the training process and the limitations of the underlying data allows us to use AI more ethically, consciously, and strategically.