{"id":524249,"date":"2024-06-28T16:09:10","date_gmt":"2024-06-28T16:09:10","guid":{"rendered":"https:\/\/www.capgemini.com\/mx-es\/?p=524249&#038;preview=true&#038;preview_id=524249"},"modified":"2025-04-03T17:37:24","modified_gmt":"2025-04-03T17:37:24","slug":"auditing-chatgpt-part-i","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/mx-es\/insights\/expert-perspectives\/auditing-chatgpt-part-i\/","title":{"rendered":"Auditing ChatGPT \u2013 part I"},"content":{"rendered":"\n<header class=\"wp-block-cg-blocks-hero-blogs header-hero-blogs\"><div class=\"container\"><div class=\"hero-blogs\"><div class=\"hero-blogs-content-wrapper\"><div class=\"row\"><div class=\"col-12\"><div class=\"header-title\"><h1>Auditing ChatGPT &#8211; Part I<\/h1><\/div><\/div><\/div><\/div><div class=\"hero-blogs-bottom\"><div class=\"header-author\"><div class=\"author-img\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/mx-es\/wp-content\/uploads\/sites\/28\/2024\/06\/Alex-Marandon.jpg?w=200&amp;quality=10\" alt=\"main author of large language models chatgpt\" loading=\"lazy\"\/><\/div><div class=\"author-name-date\"><h5 class=\"author-name\">Alex Marandon<\/h5><h5 class=\"blog-date\">Jan 12, 2024<\/h5><\/div><\/div><div class=\"brand-image\"><img decoding=\"async\" loading=\"lazy\" src=\"\/wp-content\/themes\/capgemini2020\/assets\/images\/capgemini-invent-white.svg\" alt=\"capgemini-invent\"\/><\/div><\/div><\/div><\/div><\/header>\n\n\n\n<section class=\"wp-block-cg-blocks-intro-para undefined section section--intro\"><div class=\"intro-para\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-1\"><nav class=\"article-social\"><ul class=\"social-nav\"><li class=\"ip-order-fb\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/www.capgemini.com\/mx-es\/insights\/expert-perspectives\/auditing-chatgpt-part-i\/\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"abrir en una nueva ventana\"><i aria-hidden=\"true\" class=\"icon-fb\"><\/i><span class=\"sr-only\">Facebook<\/span><\/a><\/li><li class=\"ip-order-li\"><a href=\"https:\/\/www.linkedin.com\/sharing\/share-offsite\/?url=https:\/\/www.capgemini.com\/mx-es\/insights\/expert-perspectives\/auditing-chatgpt-part-i\/\" target=\"_blank\" rel=\"noopener noreferrer\" title=\"abrir en una nueva ventana\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><span class=\"sr-only\">Linkedin<\/span><\/a><\/li><\/ul><\/nav><\/div><div class=\"col-12 col-md-11 col-lg-10\"><h2 class=\"intro-para-title\">A Chorus of Disruption: From Cave Paintings to Large Language Models<\/h2><p class=\"intro-para-content\">Since its release in November 2022, ChatGPT has revolutionized our society, captivating users with its remarkable capabilities. Its rapid and widespread adoption is a testament to its transformative potential. At the core of this chatbot lies the GPT-4 language model (or GPT-3.5 for the free version), developed by OpenAI. We have since witnessed an explosive proliferation of comparable models, such as Google Bard, Llama, and Claude. But what exactly are these models and what possibilities do they offer? More importantly, are the publicized risks justifiable and what measures can be taken to ensure safe and accountable utilization of these models?<br><br><strong>In this first part of our two-part article, we will discuss the following:<\/strong><\/p><ul class=\"box-list\"><li><a href=\"#llm\">What is an LLM?<\/a><\/li><li><a href=\"#history\">What is the technological history of ChatGPT?<\/a><\/li><li><a href=\"#talking\">Why is everyone talking about LLMs?<\/a><\/li><li><a href=\"#what\">What can you do with an LLM?<\/a><\/li><\/ul><\/div><\/div><\/div><\/div><\/section>\n\n\n\n<section class=\"wp-block-cg-blocks-group undefined section section--article-content\"><div class=\"article-main-content\"><div class=\"container\"><div class=\"row\"><div class=\"col-12 col-md-11 col-lg-10 offset-md-1 offset-lg-1\"><div class=\"article-text article-quote-text\">\n<h2 class=\"wp-block-heading\" id=\"llm\" style=\"font-size:28px\">What is Large Language Models (LLM)?<\/h2>\n\n\n\n<p>Artificial intelligence (AI) is a technological field that aims to <a href=\"https:\/\/www.larousse.fr\/encyclopedie\/divers\/intelligence_artificielle\/187257\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">give human intelligence capabilities to machines<\/a>. A generative AI is an artificial intelligence that can generate content, such as text or images. Within generative AIs, foundation models are recent developments often described as the fundamental building blocks behind such applications as DALL-E or Midjourney. In the case of text-generating AI, these are referred to as Large Language Models (LLMs), of which the Generative Pre-trained Transformer (GPT) is one example made popular by <a href=\"https:\/\/openai.com\/blog\/chatgpt\" target=\"_blank\" rel=\"noreferrer noopener\">ChatGPT<\/a>. More complete definitions of these concepts are given in Figure 1 below.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2024\/01\/Auditing-ChatGPT-infographic.png\" alt=\"\" class=\"wp-image-984256\"\/><figcaption class=\"wp-element-caption\"><center>Figure 1: Definitions of key concepts around LLMs4<\/center><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"history\">The technological history of the ChatGPT LLM<\/h3>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>In 2017, a team of researchers created a new type of model within Natural Language Processing (NLP) called Transformer. It achieved spectacular performance for sequential-data tasks, such as text or temporal data. By using a specific technology called \u2018attention mechanism\u2019, published in 2015, the Transformer model pushed the limits of previous models, particularly the length of texts processed and\/or generated.&nbsp;<\/p>\n\n\n\n<p>In 2018,<strong> <\/strong>OpenAI created a model inspired by Transformer architecture (the decoder stack in particular). The main reason for this was that Transformer, with its properties of masked attention, excels in text generation. The result was the first <a href=\"https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Generative Pre-trained Transformer<\/a>. The same year saw the release of BERT, a <a href=\"https:\/\/www.capgemini.com\/about-us\/technology-partners\/google-cloud\/\">Google<\/a> NLP model, which was also inspired by Transformers. Together, BERT and GPT launched the era of LLMs.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Improving the performance of its model over BERT LLM variants, OpenAI released GPT-2 in 2019 and GPT-3 in 2020. These two models benefited from an important breakthrough: meta-learning models. Meta-learning is a paradigm of Machine Learning (ML) in which the model &#8220;learns how to learn.&#8221; For example, the model can respond to tasks other than those for which it has been trained.&nbsp;&nbsp;<\/p>\n\n\n\n<p>OpenAI&#8217;s aim is for GPT <strong><a href=\"https:\/\/www.capgemini.com\/insights\/expert-perspectives\/generative-language-models-and-the-future-of-ai\/\">Large Language Models<\/a><\/strong> to be able to perform any NLP task with only an instruction and possibly a few examples. There would be no need for a specific database to train them for each task. OpenAI has succeeded in making meta-learning<strong> <\/strong>a strength, thanks to increasingly large architectures and databases massively retrieved from the Internet.&nbsp;&nbsp;<\/p>\n\n\n\n<p>To take its technology further, OpenAI moved beyond NLP by adapting its models for images. In 2021 and 2022, OpenAI published DALL-E 1 and DALL-E 2, two text-to-image generators.<sup>10<\/sup> These generators enabled OpenAI to make GPT-4 a multi-modal model, one that can understand several types of data.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Next, OpenAI released InstructGPT (GPT 3.5), which was designed to better meet user demands and mitigate risk. This was the version OpenAI launched in late 2022. But in March 2023, OpenAI released an even more powerful and secure version: the premium <a href=\"https:\/\/arxiv.org\/pdf\/2303.08774.pdf\">GPT-4<\/a>. Unlike preceding versions, GPT-3.5 and GPT-4 gained strong commercial interest. OpenAI has now adopted a closed source ethos \u2013 no longer revealing how the models work \u2013 and become a lucrative company (it was originally a non-profit association). Looking to the future, we can expect OpenAI to try to push the idea of a prompt for all tasks and all types of data even further.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"talking\">Why is everyone talking about Large language models?<\/h3>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Only those currently living under a rock will not have heard something about ChatGPT in recent months. The fact that it made half the business world ecstatic and the other half anxious should tell you how popular it has become. But let\u2019s take a closer look at the reasons why.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-openai-s-two-remarkable-feats\">OpenAI\u2019s two remarkable feats\u00ad\u00ad<\/h4>\n\n\n\n<p>With the development of meta-learning, OpenAI created an ultra-versatile model capable of providing accurate responses to all kinds of requests \u2013 even those it has never encountered before. In fact, GPT-4 achieves better results on specific tasks than specialized models.&nbsp;<\/p>\n\n\n\n<p>In addition to the technological leaps, OpenAI has developed democratization. By deploying its technology in the form of an accessible chatbot (ChatGPT) with a simple interface, OpenAI has made it possible for everyone to utilize this powerful language model\u2019s capabilities. This public access also enables OpenAI to collect more data and feedback used by the model.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-rapid-adoption-nbsp\">Rapid adoption <strong>&nbsp;<\/strong><\/h4>\n\n\n\n<p>The rapid adoption of GPT technology via the ChatGPT LLM has been unprecedented. Never has an internet platform or technology been adopted so rapidly (see Figure 2). <a href=\"https:\/\/explodingtopics.com\/blog\/chatgpt-users\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">ChatGPT now boasts 200 million users and two billion visits per month.<\/a>&nbsp;&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2024\/01\/Figure-2-Speed-of-reaching-100-million-users-in-months.png\" alt=\"\" class=\"wp-image-986300\"\/><figcaption class=\"wp-element-caption\"><center>Figure 2: Speed of reaching 100 million users in months.<sup>13<\/sup><\/center><\/figcaption><\/figure>\n\n\n\n<p>The number of Large Language Models is exploding, with competitors coming from Google (Bard), Meta (Llama), and HuggingFace (HuggingChat, a French open-source version). There is also a surge in new applications. For example, ChatGPT LLMs have been implemented in search engines and Auto-GPT, which latter turns GPT-4 into an autonomous agent. This remarkable progress is stimulating a new wave of research, with LLM publications growing exponentially (Figure 3).&nbsp;&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2024\/01\/Auditing-ChatGPT-infographic-3.png\" alt=\"\" class=\"wp-image-984259\"\/><figcaption class=\"wp-element-caption\"><center>Figure 3: Cumulative number of scientific publications on LLMs.<\/center><\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-opportunities-fantasies-and-fears\">Opportunities, fantasies, and fears<\/h4>\n\n\n\n<p>The new standard established by GPT-4 has broadened the range of possible use cases. As a result, many institutions are looking to exploit them. For example, some hospitals are using them to improve and automate the extraction of medical conditions from patient records.&nbsp;&nbsp;<\/p>\n\n\n\n<p>On the other hand, these same breakthroughs in performance have given rise to a host of fears: job insecurity, exam cheating, privacy threats, etc. Many recent articles explore this growing anxiety, which now seems justified \u2013 Elon Musk and Geoffrey Hinton are just two of the many influential tech figures now raising the alarm, calling it a new <a href=\"https:\/\/www.goodintech.org\/preview\/EventDetails.html?id=31\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">\u2018code red.\u2019<\/a>&nbsp;&nbsp;<\/p>\n\n\n\n<p>However, as is often the case with technological advances, people have trouble distinguishing between real risk and irrational fear (e.g., a world in which humans hide from robots like those in <em>The Terminator<\/em>). This example explores the creation of a model that rivals or surpasses the human brain. Of course, this is inextricably linked with the formation of consciousness. Here, it is worth noting that this latter fantasy is the ultimate goal of OpenAI, namely AGI (Artificial General Intelligence).&nbsp;<\/p>\n\n\n\n<p>Whether or not these events will remain fantasies or become realities, GPT-4 and the other Large Language Models\u2019 AI are undoubtedly revolutionizing our society and represent a considerable technological milestone.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"what\"><a><\/a>What can you do with an LLM?<\/h3>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-essentially-a-chatgpt-llm-can\">Essentially, a ChatGPT LLM can:<\/h4>\n\n\n\n<ol style=\"list-style-type:1\" class=\"wp-block-list\">\n<li><strong>Generate natural language content:<\/strong> Trained specifically for this purpose, this is where they excel. They strive to adhere to the given constraints as accurately as possible.<\/li>\n\n\n\n<li><strong>Reformulate content:<\/strong> This involves providing the LLM with a base text and instruction to perform tasks, such as summarizing, translating, substituting terms, or correcting errors.<\/li>\n\n\n\n<li><strong>Retrieve content:<\/strong> It is possible to request an LLM to search for and retrieve specific information based on a corpus of data.<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-how-can-you-use-an-llm-nbsp-nbsp-nbsp-nbsp-nbsp\">How can you use an LLM?&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;<\/h4>\n\n\n\n<p>There are three possible applications of Large Language Models\u2019 AI, summarized in Figure 4. The first one is direct application, where the LLM is only used for the tasks that it can perform. This is, a priori, the use case of a chatbot like ChatGPT, which directly implements GPT-4 technology. While this is one of the most common applications, it is also one of the riskiest. This is because the ChatGPT LLM often acts like a black box and is difficult to evaluate.&nbsp;<\/p>\n\n\n\n<p>One emerging use of LLMs is the auxiliary application. To limit risks, an LLM is implemented here as an auxiliary tool within a system. For example, in a search engine, an LLM can be used as an interface for presenting the results of a search. This use case was applied to the corpus of IPCC reports.<sup>19<\/sup><sup> <\/sup>The disadvantage here is that the LLM is far from being fully exploited.&nbsp;&nbsp;<\/p>\n\n\n\n<p>In the near future, the orchestral application of ChatGPT LLMs will consume much of the research budget for large organizations. In an orchestral application, the LLM is both the interface with the user and the brain of the system in which it is implemented. The LLM therefore understands the task, calls on auxiliary tools in its system (e.g., Wolfram Alpha for mathematical calculations), and then delivers the result. Here, the LLM acts less like a black box, but the risk assessment of such a system will also depend on the auxiliary tools. The best example to date is <a href=\"https:\/\/www.blogdumoderateur.com\/auto-gpt-definition-acces-alternatives-exemples\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Auto-GPT<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2024\/01\/Figure-4-The-three-possible-applications-of-an-LLM.png\" alt=\"\" class=\"wp-image-986305\"\/><figcaption class=\"wp-element-caption\"><center>Figure 4: The three possible applications of an LLM<\/center><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-focusing-on-the-use-case-of-a-chatbot-citing-its-sources\">Focusing on the use case of a Chatbot citing its sources<\/h3>\n\n\n\n<p>One specific use case that is emerging among our customers is that of a chatbot citing its sources. This is a response to the inability of Large Language Models\u2019 AI to interpret results (i.e., the inability to understand which sources the LLM has used and why).<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2024\/01\/Auditing-ChatGPT-infographic-5.png\" alt=\"\" class=\"wp-image-984261\"\/><figcaption class=\"wp-element-caption\"><center>Figure 5: Technical diagram of a conversational agent quoting its sources<\/center><\/figcaption><\/figure>\n\n\n\n<p>To delve into the technical details of the chatbot citing its sources (The relevant pattern \u2013 illustrated in Figure 5 \u2013 Is called Retrieval Augmented Generation or \u2018RAG\u2019), the model takes a user request as input, which the model then transforms into an embedding (i.e., a word or sentence vectorization that captures semantic and syntactic relationships). The model has a corpus of texts already transformed into embeddings. The goal is then to find the embeddings within the corpus that are closest to the query embedding. This usually involves techniques that find the nearest neighbours\u2019 algorithms. Once we have identified the corpus elements that can help with the response, we can pass them to an LLM to synthesize the answer. Alongside the response, we can provide the elements that were used to generate it. The LLM then serves as an interface for presenting the search engine&#8217;s results. This \u2018RAG\u2019 approach therefore facilitates the decoupling of factual information provided by the sources from the semantic analysis provided by the LLM, leading to better auditability of the results provided by the Chatbot. &nbsp;<\/p>\n\n\n\n<p>Read more in <a href=\"https:\/\/www.capgemini.com\/insights\/expert-perspectives\/auditing-chatgpt-part-ii\/\">Auditing ChatGPT \u2013 part II<\/a><a id=\"_msocom_1\"><\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/section>\n\n\n\n<section class=\"wp-block-cg-blocks-wrapper-people-slider section section--expert-slider wrapper-people-slider undefined\"><div class=\"container\"><div class=\"content-title\"><h3 data-maxlength=\"34\">Authors<\/h3><\/div><\/div><div class=\"slider slider-boxed\"><div class=\"container\"><div class=\"slider-window\"><div class=\"slider-list\">\n\t\t<div class=\"slide\">\n\t\t\t<div class=\"box\">\n\t\t\t\t<div class=\"row\">\n\t\t\t\t\t<div class=\"col-md-6 col-lg-4 box-img-wrapper\">\n\t\t\t\t\t\t<img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/www.capgemini.com\/mx-es\/wp-content\/uploads\/sites\/28\/2024\/06\/Alex-Marandon.jpg\" alt=\"main author of large language models chatgpt\"\/>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div class=\"col-md-6 col-lg-8 box-inner\">\n\t\t\t\t\t\t<div class=\"row title-social-media-header\">\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 mbl-social-icon\">\n\t\t\t\t\t\t\t\t<ul class=\"social-nav\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"subnav-button--form\" href=\"javascript:void(0)\" aria-label=\"contacta con nosotros\" data-bs-toggle=\"modal\" data-bs-target=\"#WPB8e79b631_af11_434c_941a_d694c7e6f1bc\" data-bs-expert-title=\"1\">\n\t\t\t\t\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"ico-form\"><\/i>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li><a aria-label=\"Linkedin\" target=\"_blank\" title=\"abrir en una nueva ventana\" href=\"https:\/\/www.linkedin.com\/in\/alexmarandon\/\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><\/a><\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 box-container\">\n\t\t\t\t\t\t\t\t<div class=\"box-title\">\n\t\t\t\t\t\t\t\t\t<h3 class=\"people-profile-title\">Alex Marandon<\/h3>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span>Vice President &amp; Global Head of generative AI Accelerator, Capgemini Invent<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"col-md-12 col-lg-6 social-box-container dkt-social-icon\">\n\t\t\t\t\t\t\t\t<ul class=\"social-nav\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"subnav-button--form\" href=\"javascript:void(0)\" aria-label=\"contacta con nosotros\" data-bs-toggle=\"modal\" data-bs-target=\"#WPB8e79b631_af11_434c_941a_d694c7e6f1bc\" data-bs-expert-title=\"Alex Marandon\">\n\t\t\t\t\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"ico-form\"><\/i>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<li><a aria-label=\"Linkedin\" target=\"_blank\" title=\"abrir en una nueva ventana\" href=\"https:\/\/www.linkedin.com\/in\/alexmarandon\/\"><i aria-hidden=\"true\" class=\"icon-li\"><\/i><\/a><\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"people-info\">Alex brings over 20 years of experience in the tech and data space, beginning his career as a CTO in startups and later leading data science and engineering in the travel sector. Eight years ago, he joined Capgemini Invent, where he has been at the forefront of driving digital innovation and transformation for his clients. He has a strong track record in designing large-scale data ecosystems, especially within the industrial sector. Currently, as the Global Lead of Capgemini Invent\u2019s generative AI Acceleration Lab, Alex crafts Gen AI go-to-market strategies, develops assets, upskills teams, and assists clients in scaling AI and Gen AI solutions from proof of concept to value generation.<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"modal\" id=\"WPB8e79b631_af11_434c_941a_d694c7e6f1bc\">\n\t\t\t\t\t<div class=\"modal-dialog modal-dialog-centered modal-lg modal-dialog-scrollable landing-page-modals\">\n\t\t\t\t\t\t<div class=\"modal-content\">\n\t\t\t\t\t\t\t<div class=\"modal-header\">\n\t\t\t\t\t\t\t\t<button type=\"button\" class=\"btn-close\" aria-label=\"fermer\" data-bs-dismiss=\"modal\"><\/button>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"modal-body\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"logo-wrapper\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/mx-es\/wp-content\/themes\/capgemini2025\/assets\/images\/capgemini-invent.svg\"\n\t\t\t\t\t\t\t\t\t\talt=\"Get in touch\" class=\"light\" \/>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.capgemini.com\/mx-es\/wp-content\/themes\/capgemini2025\/assets\/images\/capgemini-invent-white.svg\"\n\t\t\t\t\t\t\t\t\t\talt=\"Get in touch\" class=\"dark\" \/>\n\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<h4 class=\"form-title\">Get in touch<\/h4>\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t<div class=\"mf_forms__container \">\n\t\t\t\t\t\t\t\t<form id=\"mf_form_344241_2076213840\" class=\"mf_form__form row needs-validation\" action=\"\" method=\"post\" enctype=\"multipart\/form-data\" data-nosnippet>\n\n\t\t\t\t\t\t\t\t<input type=\"hidden\" name=\"form_id\" data-validate=\"number\" class=\"mf_form__id\" value=\"344241\" \/>\n\t\t\t\t<input type=\"hidden\" name=\"form_title\" class=\"mf_form__title\" value=\"\" \/>\n\t\t\t\t<input type=\"hidden\" name=\"wp_rest_nonce\" class=\"mf_form__wp_rest_nonce\" value=\"667204ada3\" \/>\n\n\t\t\t\t<input type=\"hidden\" name=\"mf_form_conditional_logic\" class=\"mf_form__title conditional_logic\" value=\"\" \/>\n\t\t\t\t\n\t\t\t\t<input type=\"hidden\" name=\"mf_form_fields_country_dpo_field\" class=\"mf_form__title conditional_logic mf_form_fields_country_dpo_field\" value=\"\" \/>\n\n\t\t\t\t<!-- Anti-spam field -->\n\t\t\t\t<input type=\"text\" \n\t\t\t\t\tname=\"website_url\" \n\t\t\t\t\tclass=\"mf_field__url\" \n\t\t\t\t\tautocomplete=\"off\"\n\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\tstyle=\"position:absolute;left:-9999px;width:1px;height:1px;opacity:0;pointer-events:none;\" \n\t\t\t\t\/>\n\t\t\t<\/form>\n\n\t\t\t<div class=\"mf_form__success\" tabindex=\"0\" role=\"alert\">\n\t\t\t\t\t\t\t\t<p>Gracias! 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Dive deep into the history and applications.<\/p>\n","protected":false},"author":290,"featured_media":524250,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"cg_dt_proposed_to":[],"cg_seo_hreflang_relations":"[]","cg_seo_canonical_relation":"","cg_seo_hreflang_x_default_relation":"{\"uuid\":\"288418ea-82cd-4725-bc77-a738736665ff\",\"blogId\":\"\",\"domain\":\"\",\"sitePath\":\"\",\"postLink\":\"\",\"postId\":null,\"isSaved\":true,\"isCrossLink\":false,\"hasCrossLink\":false}","cg_dt_approved_content":true,"cg_dt_mandatory_content":false,"cg_dt_notes":"","cg_dg_source_changed":false,"cg_dt_link_disabled":false,"_yoast_wpseo_primary_brand":"79","_jetpack_memberships_contains_paid_content":false,"footnotes":"","featured_focal_points":""},"categories":[1],"tags":[],"brand":[79],"service":[162],"industry":[],"partners":[],"blog-topic":[49],"content-group":[],"class_list":["post-524249","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","brand-capgemini-invent","service-data-ai","blog-topic-data-and-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.8 (Yoast SEO v22.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ChatGPT: Evolution, Impact, and GPT-4 Insights | Capgemini Invent<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.capgemini.com\/mx-es\/insights\/expert-perspectives\/auditing-chatgpt-part-i\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ChatGPT: Evolution, Impact, and GPT-4 Insights | Capgemini Invent\" \/>\n<meta property=\"og:description\" content=\"Explore ChatGPT&#039;s evolution, impact, and GPT-4 insights. Uncover the role of large language models in reshaping AI capabilities. Dive deep into the history and applications.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.capgemini.com\/mx-es\/insights\/expert-perspectives\/auditing-chatgpt-part-i\/\" \/>\n<meta property=\"og:site_name\" content=\"Capgemini Mexico\" \/>\n<meta property=\"article:published_time\" content=\"2024-06-28T16:09:10+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-03T17:37:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.capgemini.com\/mx-es\/wp-content\/uploads\/sites\/28\/2025\/10\/New-Web-preview-global.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"627\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Alex Marandon\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Auditing ChatGPT \u2013 part I\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" 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