{"id":4744,"date":"2025-09-24T11:25:24","date_gmt":"2025-09-24T11:25:24","guid":{"rendered":"https:\/\/palmer-consulting.com\/full-definition-of-generative-ai\/"},"modified":"2025-09-24T11:25:24","modified_gmt":"2025-09-24T11:25:24","slug":"full-definition-of-generative-ai","status":"publish","type":"post","link":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/","title":{"rendered":"Full definition of Generative AI"},"content":{"rendered":"

Full definition of generative AI<\/h1>\n

1) Academic definition (rigorous)<\/h2>\n

Generative AI<\/strong> covers machine learning methods that aim to model the distribution of data<\/strong>. text,image,audio,vide\u02cao,code,etc.text, image, audio, video, code, etc.<\/span>t<\/span>e<\/span>x<\/span>t<\/span>e<\/span>,<\/span>ima<\/span>g<\/span>e<\/span>,<\/span>a<\/span>u<\/span>d<\/span>i<\/span>o<\/span>,<\/span>v<\/span>i<\/span>d<\/span>e<\/span>\u02ca<\/span><\/span><\/span><\/span><\/span><\/span>o<\/span>,<\/span>co<\/span>d<\/span>e<\/span>,<\/span>e<\/span>t<\/span>c<\/span>.<\/span><\/span><\/span><\/span> to generate plausible new samples<\/strong>. Formally, a generative model learns p\u03b8(x)p_\\theta(x)<\/span>p<\/span>\u03b8<\/span><\/span><\/span><\/span><\/span><\/span><\/span>(<\/span>x<\/span>)<\/span><\/span><\/span><\/span><\/span> (or p\u03b8(x\u2223c)p_\\theta(x \\mid c)<\/span>p<\/span>\u03b8<\/span><\/span><\/span><\/span><\/span><\/span><\/span>(<\/span>x<\/span>\u2223<\/span><\/span><\/span>c<\/span>)<\/span><\/span><\/span><\/span> conditioned by context cc<\/span>c<\/span><\/span><\/span><\/span>) based on a training corpus, then sample<\/strong> new bodies x\\*x^\\*<\/span>x<\/span>\\*<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> that respect the statistical regularities we’ve learned.<\/p>\n

\n

In other words, instead of just predicting<\/strong> or classifying<\/strong>, we create<\/strong> content in line with the style and structures of the original data.<\/p>\n<\/blockquote>\n

\n

2) Intuition and principles<\/h2>\n
    \n
  • \n

    Objective<\/strong>: to learn the “shape” of the data (its distribution) so as to be able to produce new, credible variants.<\/p>\n<\/li>\n

  • \n

    Two key questions<\/strong>:<\/p>\n

      \n
    1. \n

      How to learn p(x)p(x)<\/span>p<\/span>(<\/span>x<\/span>)<\/span><\/span><\/span><\/span>?<\/em> (optimization, loss function, architecture)<\/p>\n<\/li>\n

    2. \n

      How to sample?<\/em> (generation procedure, style and diversity control)<\/p>\n<\/li>\n<\/ol>\n<\/li>\n

    3. \n

      Model types<\/strong>:<\/p>\n

        \n
      • \n

        With explicit likelihood<\/em> (a bound or the log-likelihood is maximized)<\/p>\n<\/li>\n

      • \n

        Adversarial implications<\/em> (we don’t calculate p(x)p(x)<\/span>p<\/span>(<\/span>x<\/span>)<\/span><\/span><\/span><\/span>a discriminator is “fooled”)<\/p>\n<\/li>\n

      • \n

        Noise-based<\/em> (progressive denoising is learned for sampling)<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

        \n

        3) Main families of generative models<\/h2>\n
          \n
        • \n

          Autoregressive transformers (LLM)<\/strong><\/p>\n

            \n
          • \n

            Principle<\/em>: predict the next token<\/strong> p(xt\u2223x<t)p(x_t \\mid x_{<t})<\/span>p<\/span>(<\/span>x<\/span>t<\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2223<\/span><\/span><\/span>x<\/span><<\/span>t<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span>.<\/p>\n<\/li>\n

          • \n

            Strengths<\/em>: text, code, recent multimodal; very good at “prompt” composition and packaging.<\/p>\n<\/li>\n

          • \n

            Control<\/em>: temperature, top-k, top-p (nucleus), format constraints (JSON), external tools (RAG, functions).<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

          • \n

            GANs (Generative Adversarial Networks)<\/strong><\/p>\n

              \n
            • \n

              Principle<\/em>: a generator<\/strong> produces examples, a discriminator<\/strong> tries to distinguish them from the real thing; zero-sum game.<\/p>\n<\/li>\n

            • \n

              Strengths<\/em>: high-fidelity images, style and detail;<\/p>\n<\/li>\n

            • \n

              Limitations<\/em>: drive instabilities, collapse mode, sometimes fragile metrics.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

            • \n

              VAEs (Variational Autoencoders)<\/strong><\/p>\n

                \n
              • \n

                Principle<\/em>: encode xx<\/span>x<\/span><\/span><\/span><\/span> in one latent space<\/strong> zz<\/span>z<\/span><\/span><\/span><\/span>rebuild xx<\/span>x<\/span><\/span><\/span><\/span> since zz<\/span>z<\/span><\/span><\/span><\/span> with probabilistic regularization.<\/p>\n<\/li>\n

              • \n

                Strengths<\/em>: interpretable latents, smooth interpolation, conditional generation.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

              • \n

                Diffusion \/ Score-based models<\/strong><\/p>\n

                  \n
                • \n

                  Principle<\/em>: learn to remove<\/strong> added noise<\/strong> in several stages; at inference, we debudge to sample.<\/p>\n<\/li>\n

                • \n

                  Strengths<\/em>: excellent image quality, video\/3D progress; fine control via classifier-free guidance<\/em>, ControlNet.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

                • \n

                  Normalizing Flows<\/strong><\/p>\n

                    \n
                  • \n

                    Principle<\/em>: transform a simple distribution into a complex one via bijective transformations; exact log-density.<\/p>\n<\/li>\n

                  • \n

                    Strengths<\/em>: calculable likelihood;<\/p>\n<\/li>\n

                  • \n

                    Limits<\/em>: architectural constraints to remain reversible.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

                  • \n

                    Energy-based models (EBM)<\/strong><\/p>\n

                      \n
                    • \n

                      Principle<\/em>: define an energy function whose minimum corresponds to the probable data; MCMC sampling.<\/p>\n<\/li>\n

                    • \n

                      Forces<\/em>: general theoretical framework;<\/p>\n<\/li>\n

                    • \n

                      Limitations<\/em>: sampling can be costly.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                      \n

                      4) Data, training and alignment<\/h2>\n
                        \n
                      • \n

                        Data preparation<\/strong>: cleansing, deduplication, quality filtering, domain balancing, copyright management, PII.<\/p>\n<\/li>\n

                      • \n

                        Training objectives<\/strong>:<\/p>\n

                          \n
                        • \n

                          Next-token prediction<\/em> (LLM)<\/p>\n<\/li>\n

                        • \n

                          Denoising<\/em> (distribution, VAEs)<\/p>\n<\/li>\n

                        • \n

                          Adversarial loss<\/em> (GAN)<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

                        • \n

                          Optimization<\/strong>: Adam\/AdamW, LR design, gradient clipping<\/em>, mixed precision<\/em>; scaling laws<\/em> (quality \u221d model size \u00d7 data \u00d7 compute).<\/p>\n<\/li>\n

                        • \n

                          Fine-tuning & specialization<\/strong>:<\/p>\n

                            \n
                          • \n

                            SFT<\/strong> (Supervised Fine-Tuning) on high-quality demonstrations.<\/p>\n<\/li>\n

                          • \n

                            PEFT<\/strong> (LoRA\/QLoRA, adapters) to reduce memory\/compute costs.<\/p>\n<\/li>\n

                          • \n

                            RAG<\/strong> (Retrieval-Augmented Generation) to anchor<\/strong> answers to verifiable sources.<\/p>\n<\/li>\n

                          • \n

                            Human preferences<\/strong>: RLHF \/ RLAIF or DPO\/IPO\/ORPO<\/em> alternatives to control style, security and usefulness.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                            \n

                            5) Inference, control and constraints<\/h2>\n
                              \n
                            • \n

                              Sampling (text)<\/strong>: temperature (diversity), top-k (candidate vocabulary size), top-p (probability mass).<\/p>\n<\/li>\n

                            • \n

                              Output constraints<\/strong>: guided decoding<\/em>, grammars\/JSON Schema, beam search<\/em> (when deterministic coherence is preferred).<\/p>\n<\/li>\n

                            • \n

                              Image\/video control (broadcast)<\/strong>: classifier-free guidance<\/em>, ControlNet<\/em>, image-to-image<\/em>, inpainting<\/em>, IP-Adapter<\/em>.<\/p>\n<\/li>\n

                            • \n

                              Tools & agents<\/strong>: function-calling, external tools (search, code), toolformer-like<\/em>; planning and execution in loops(agentic<\/em>).<\/p>\n<\/li>\n

                            • \n

                              Performance<\/strong>: quantization (8-bit, 4-bit), KV cache<\/em>, MoE<\/em> (mixture-of-experts), batching<\/em>, distillation, speculative decoding<\/em>.<\/p>\n<\/li>\n<\/ul>\n

                              \n

                              6) Assessment (quality, reliability, safety)<\/h2>\n
                                \n
                              • \n

                                Text<\/strong>: perplexity (fluency proxy), RED\/BLUE (summaries\/trades), BERTScore\/COMET, human evals (accuracy, usefulness).<\/p>\n<\/li>\n

                              • \n

                                Image<\/strong>: FID, KID, IS, CLIPScore; human perceptual assessments.<\/p>\n<\/li>\n

                              • \n

                                Factuality & safety<\/strong>: hallucination rate, accuracy on open-book<\/em>, robustness to prompt injection<\/em>, toxicity\/bias.<\/p>\n<\/li>\n

                              • \n

                                Format compliance<\/strong>: JSON\/SQL validity, strict schemas, accuracy on constraints (units, value ranges).<\/p>\n<\/li>\n<\/ul>\n

                                \n

                                7) Major use cases<\/h2>\n
                                  \n
                                • \n

                                  Content & productivity<\/strong>: assisted copywriting, summarization, translation, visual asset generation, storyboarding<\/em>.<\/p>\n<\/li>\n

                                • \n

                                  Code & data<\/strong>: programming assistance, test generation, migration\/modernization, data synthesis<\/strong> for balancing training sets.<\/p>\n<\/li>\n

                                • \n

                                  Operations & support<\/strong>: internal assistants, RAG on document bases, dynamic SOPs, compliant chatbots<\/em>.<\/p>\n<\/li>\n

                                • \n

                                  Design, R&D, industry<\/strong>: CAD assistance, visual prototyping, simulation (synthetic data), anomaly detection.<\/p>\n<\/li>\n

                                • \n

                                  Health\/Science<\/strong>: molecular design, imaging, scientific literacy (with strong safeguards).<\/p>\n<\/li>\n

                                • \n

                                  Finance\/Insurance<\/strong>: report generation, structured document extraction, what-if<\/em> (with dedicated templates and strict control).<\/p>\n<\/li>\n<\/ul>\n

                                  \n

                                  8) Limits and risks<\/h2>\n
                                    \n
                                  • \n

                                    Hallucinations<\/strong> (LLM): fluid responses but false if not anchored<\/strong> (RAG) or poorly constrained.<\/p>\n<\/li>\n

                                  • \n

                                    Bias & representativeness<\/strong>: historical data \u2192 reproduced bias; need for debiasing<\/em> and targeted assessments.<\/p>\n<\/li>\n

                                  • \n

                                    Security<\/strong>: prompt injection<\/em>, data exfiltration<\/em>, jailbreaks; need for continuous red teaming<\/em>.<\/p>\n<\/li>\n

                                  • \n

                                    Intellectual property & rights<\/strong>: data provenance, copyright<\/em>, licensing<\/em>; logo\/visage management.<\/p>\n<\/li>\n

                                  • \n

                                    Sensitive data & confidentiality<\/strong>: PII, secrets; differential privacy<\/em>, synthetic data<\/em> with care.<\/p>\n<\/li>\n

                                  • \n

                                    Costs & footprint<\/strong>: compute\/energy; trade-offs between model size and business value.<\/p>\n<\/li>\n<\/ul>\n

                                    \n

                                    9) Governance, compliance and best practices<\/h2>\n
                                      \n
                                    • \n

                                      Responsible lifecycle<\/strong>: model cards<\/em>, data sheets<\/em>, prompt logging, version tracking.<\/p>\n<\/li>\n

                                    • \n

                                      Pre-release checks<\/strong>: off-domain evaluation, attack tests (security), guardrails<\/em>, rate limiting<\/em>.<\/p>\n<\/li>\n

                                    • \n

                                      Documentary anchoring (RAG<\/strong> ): citations\/justifications, source grounding<\/em>, update management.<\/p>\n<\/li>\n

                                    • \n

                                      Media authenticity<\/strong>: watermarks, C2PA (provenance), content authenticity<\/em>.<\/p>\n<\/li>\n

                                    • \n

                                      Regulatory frameworks<\/strong>:trusted AI<\/strong> principles (fairness, explicability, robustness) and increasing requirements (e.g. obligations by risk level).<\/p>\n<\/li>\n<\/ul>\n

                                      \n

                                      10) Structuring trends<\/h2>\n
                                        \n
                                      • \n

                                        Native multimodal<\/strong> (text-image-audio-video-sensors) and reasoning tools<\/strong> (code, search, business tools).<\/p>\n<\/li>\n

                                      • \n

                                        Specialist vs. foundation models<\/strong>: combining a generalist LLM + light experts via routing\/MoE<\/em>.<\/p>\n<\/li>\n

                                      • \n

                                        Efficiency<\/strong>: small,<\/em> high-performance language models<\/em> for targeted domains, quantified and adapted on-prem\/edge<\/em>.<\/p>\n<\/li>\n

                                      • \n

                                        Constrained generation<\/strong>: structured<\/strong> output (JSON\/SQL), direct integration into workflows and databases.<\/p>\n<\/li>\n

                                      • \n

                                        Next-generation security<\/strong>: prompt attack detection, contextual content moderation<\/em>, policy engines<\/em>.<\/p>\n<\/li>\n<\/ul>\n

                                        \n

                                        11) Glossary of this article<\/h2>\n
                                          \n
                                        • \n

                                          Autoregressive<\/strong>: generates one token at a time, conditioned on history.<\/p>\n<\/li>\n

                                        • \n

                                          Temperature<\/strong>: controls diversity<\/em> (high = more creative, low = more conservative).<\/p>\n<\/li>\n

                                        • \n

                                          Top-k \/ Top-p<\/strong>: restrict candidate space to stabilize style.<\/p>\n<\/li>\n

                                        • \n

                                          LoRA\/PEFT<\/strong>: refining a large model with few trainable parameters.<\/p>\n<\/li>\n

                                        • \n

                                          RAG<\/strong>: retrieve relevant documents and anchor<\/strong> their generation.<\/p>\n<\/li>\n

                                        • \n

                                          Diffusion<\/strong>: progressive denoising of<\/strong> a noisy signal.<\/p>\n<\/li>\n

                                        • \n

                                          FID<\/strong>: generated image quality metric (statistical proximity to reality).<\/p>\n<\/li>\n<\/ul>\n

                                          \n

                                          12) Project mini-checklist (operational)<\/h2>\n
                                            \n
                                          1. \n

                                            Framing usage<\/strong>: business cases, risks, value metrics.<\/p>\n<\/li>\n

                                          2. \n

                                            Select approach<\/strong>: LLM + RAG? Diffusion? VAE ? <\/p>\n<\/li>\n

                                          3. \n

                                            Data & rights<\/strong>: quality, governance, compliance.<\/p>\n<\/li>\n

                                          4. \n

                                            Experiment<\/strong>: prompting<\/em> + constraints + safeguards.<\/p>\n<\/li>\n

                                          5. \n

                                            Evaluate<\/strong>: quality, factuality, safety, cost.<\/p>\n<\/li>\n

                                          6. \n

                                            Industrialize<\/strong>: monitoring, feedback loops, updates.<\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

                                            Full definition of generative AI 1) Academic definition (rigorous) Generative AI covers machine learning methods that aim to model the distribution of data. text,image,audio,vide\u02cao,code,etc.text, image, audio, video, code, etc.texte,image,audio,vide\u02cao,code,etc. to generate plausible new samples. Formally, a generative model learns p\u03b8(x)p_\\theta(x)p\u03b8(x) (or p\u03b8(x\u2223c)p_\\theta(x \\mid c)p\u03b8(x\u2223c) conditioned by context ccc) based on a training corpus, then sample […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[78],"tags":[],"class_list":["post-4744","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence"],"acf":[],"yoast_head":"\nFull definition of Generative AI | Palmer<\/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:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Full definition of Generative AI | Palmer\" \/>\n<meta property=\"og:description\" content=\"Full definition of generative AI 1) Academic definition (rigorous) Generative AI covers machine learning methods that aim to model the distribution of data. text,image,audio,vide\u02cao,code,etc.text, image, audio, video, code, etc.texte,image,audio,vide\u02cao,code,etc. to generate plausible new samples. Formally, a generative model learns p\u03b8(x)p_theta(x)p\u03b8(x) (or p\u03b8(x\u2223c)p_theta(x mid c)p\u03b8(x\u2223c) conditioned by context ccc) based on a training corpus, then sample […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/\" \/>\n<meta property=\"og:site_name\" content=\"Palmer\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-24T11:25:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/palmer-consulting.com\/wp-content\/uploads\/2023\/09\/social-graph-palmer.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"675\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Laurent Zennadi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Laurent Zennadi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/\"},\"author\":{\"name\":\"Laurent Zennadi\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#\\\/schema\\\/person\\\/7ea52877fd35814d1d2f8e6e03daa3ed\"},\"headline\":\"Full definition of Generative AI\",\"datePublished\":\"2025-09-24T11:25:24+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/\"},\"wordCount\":1001,\"publisher\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#organization\"},\"articleSection\":[\"Artificial intelligence\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/\",\"url\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/\",\"name\":\"Full definition of Generative AI | Palmer\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#website\"},\"datePublished\":\"2025-09-24T11:25:24+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/full-definition-of-generative-ai\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/home\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Full definition of Generative AI\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#website\",\"url\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/\",\"name\":\"Palmer\",\"description\":\"Evolve at the speed of change\",\"publisher\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#organization\",\"name\":\"Palmer\",\"url\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/palmer-consulting.com\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/Palmer_Logo_Full_PenBlue_1x1-2.jpg\",\"contentUrl\":\"https:\\\/\\\/palmer-consulting.com\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/Palmer_Logo_Full_PenBlue_1x1-2.jpg\",\"width\":480,\"height\":480,\"caption\":\"Palmer\"},\"image\":{\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/company\\\/palmer-consulting\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/palmer-consulting.com\\\/en\\\/#\\\/schema\\\/person\\\/7ea52877fd35814d1d2f8e6e03daa3ed\",\"name\":\"Laurent Zennadi\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g\",\"caption\":\"Laurent Zennadi\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Full definition of Generative AI | Palmer","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/","og_locale":"en_US","og_type":"article","og_title":"Full definition of Generative AI | Palmer","og_description":"Full definition of generative AI 1) Academic definition (rigorous) Generative AI covers machine learning methods that aim to model the distribution of data. text,image,audio,vide\u02cao,code,etc.text, image, audio, video, code, etc.texte,image,audio,vide\u02cao,code,etc. to generate plausible new samples. Formally, a generative model learns p\u03b8(x)p_theta(x)p\u03b8(x) (or p\u03b8(x\u2223c)p_theta(x mid c)p\u03b8(x\u2223c) conditioned by context ccc) based on a training corpus, then sample […]","og_url":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/","og_site_name":"Palmer","article_published_time":"2025-09-24T11:25:24+00:00","og_image":[{"width":1200,"height":675,"url":"https:\/\/palmer-consulting.com\/wp-content\/uploads\/2023\/09\/social-graph-palmer.png","type":"image\/png"}],"author":"Laurent Zennadi","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Laurent Zennadi","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/#article","isPartOf":{"@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/"},"author":{"name":"Laurent Zennadi","@id":"https:\/\/palmer-consulting.com\/en\/#\/schema\/person\/7ea52877fd35814d1d2f8e6e03daa3ed"},"headline":"Full definition of Generative AI","datePublished":"2025-09-24T11:25:24+00:00","mainEntityOfPage":{"@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/"},"wordCount":1001,"publisher":{"@id":"https:\/\/palmer-consulting.com\/en\/#organization"},"articleSection":["Artificial intelligence"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/","url":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/","name":"Full definition of Generative AI | Palmer","isPartOf":{"@id":"https:\/\/palmer-consulting.com\/en\/#website"},"datePublished":"2025-09-24T11:25:24+00:00","breadcrumb":{"@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/palmer-consulting.com\/en\/full-definition-of-generative-ai\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/palmer-consulting.com\/en\/home\/"},{"@type":"ListItem","position":2,"name":"Full definition of Generative AI"}]},{"@type":"WebSite","@id":"https:\/\/palmer-consulting.com\/en\/#website","url":"https:\/\/palmer-consulting.com\/en\/","name":"Palmer","description":"Evolve at the speed of change","publisher":{"@id":"https:\/\/palmer-consulting.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/palmer-consulting.com\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/palmer-consulting.com\/en\/#organization","name":"Palmer","url":"https:\/\/palmer-consulting.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/palmer-consulting.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/palmer-consulting.com\/wp-content\/uploads\/2023\/08\/Palmer_Logo_Full_PenBlue_1x1-2.jpg","contentUrl":"https:\/\/palmer-consulting.com\/wp-content\/uploads\/2023\/08\/Palmer_Logo_Full_PenBlue_1x1-2.jpg","width":480,"height":480,"caption":"Palmer"},"image":{"@id":"https:\/\/palmer-consulting.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.linkedin.com\/company\/palmer-consulting\/"]},{"@type":"Person","@id":"https:\/\/palmer-consulting.com\/en\/#\/schema\/person\/7ea52877fd35814d1d2f8e6e03daa3ed","name":"Laurent Zennadi","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/110e8a99f01ca2c88c3d23656103640dc17e08eac86e26d0617937a6846b4007?s=96&d=mm&r=g","caption":"Laurent Zennadi"}}]}},"_links":{"self":[{"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/posts\/4744","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/comments?post=4744"}],"version-history":[{"count":0,"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/posts\/4744\/revisions"}],"wp:attachment":[{"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/media?parent=4744"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/categories?post=4744"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/palmer-consulting.com\/en\/wp-json\/wp\/v2\/tags?post=4744"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}