{"id":71682,"date":"2026-09-04T16:33:23","date_gmt":"2026-09-04T16:33:23","guid":{"rendered":"https:\/\/www.arimetrics.com\/?post_type=encyclopedia&#038;p=71682"},"modified":"2026-09-12T19:14:24","modified_gmt":"2026-09-12T19:14:24","slug":"fine-tuning","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/fine-tuning","title":{"rendered":"Fine-tuning"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2026\/09\/fine-tuning-square-es.png\" alt=\"Fine-tuning\" class=\"boxpad alignright\" style=\"margin-top:0\" width=\"300\" height=\"300\" \/><strong>Definition:<\/strong><\/p>\n<p><strong>Fine-tuning<\/strong> is a training process that adapts a previously trained artificial intelligence model to a specific task, domain or behavior using additional data.<\/p>\n<p>Instead of developing a model from scratch, it builds on its existing capabilities and adjusts parameters in the model or in added components to adapt it. It is not the same as writing an instruction or attaching a document to a conversation.<\/p>\n\n<h2>From pretraining to fine-tuning<\/h2>\n<p>Fine-tuning is part of <strong>transfer learning<\/strong>: taking what a model has learned and adapting it to another problem. It is used in areas such as computer vision, speech recognition and language processing.<\/p>\n<p>Pretraining provides general capabilities from large amounts of data. Subsequent fine-tuning directs those capabilities toward more specific needs, usually with less data and fewer resources than full training from scratch.<\/p>\n<p>In language processing, models such as <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/google-bert\">BERT<\/a> helped spread the use of this approach for specific tasks. In <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/generative-ai\">generative models<\/a>, it also allows work on instruction following, response formats or specialized behaviors.<\/p>\n<h2>How fine-tuning is performed<\/h2>\n<p>The process begins by defining what should improve compared with the original model. That need determines the data to prepare and the tests that will be used to assess the result.<\/p>\n<p>In <strong>supervised fine-tuning<\/strong>, the training dataset contains inputs paired with expected responses. The model processes these examples, and training modifies the selected parameters to bring its results closer to the desired behavior.<\/p>\n<p>There are different ways to carry out this adaptation:<\/p>\n<ul>\n<li><strong>Full fine-tuning:<\/strong> updates all the model&#8217;s parameters, with greater memory and processing requirements.<\/li>\n<li><strong>Fine-tuning part of the model:<\/strong> keeps some components unchanged and trains others.<\/li>\n<li><strong>Parameter-efficient adaptation:<\/strong> uses techniques such as LoRA to train a small set of additional parameters while keeping the original weights fixed during fine-tuning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/huggingface.co\/docs\/peft\/main\/en\/conceptual_guides\/lora\" target=\"_blank\" rel=\"noopener\">LoRA<\/a> can reduce the resources needed to adapt large models. However, it does not remove the need to prepare good data and evaluate the resulting behavior.<\/p>\n<h2>What fine-tuning can be useful for<\/h2>\n<p>Fine-tuning can help when a task is repeated and requires behavior that the original model does not achieve consistently enough. Its applications include the following:<\/p>\n<ul>\n<li><strong>Classification:<\/strong> assigning texts or images to defined categories.<\/li>\n<li><strong>Information extraction:<\/strong> identifying fields and relationships according to consistent criteria.<\/li>\n<li><strong>Structured generation:<\/strong> producing responses with a specific format or organization.<\/li>\n<li><strong>Style adaptation:<\/strong> following writing patterns present in a set of examples.<\/li>\n<li><strong>Specialization:<\/strong> improving the handling of terminology and tasks specific to a domain.<\/li>\n<\/ul>\n<p>Its usefulness should be checked against the model without fine-tuning. An improvement in one specific task does not mean the model is better at every other task.<\/p>\n<h2>Data and evaluation of the result<\/h2>\n<p>Training data should represent the actual task and follow consistent criteria. Contradictory responses, incorrect labels or excessive repetition can teach unwanted patterns.<\/p>\n<p>Data that will not be used for training must also be set aside. Evaluating only with the same examples the model has already seen can give a misleading impression of its quality.<\/p>\n<p><strong>Overfitting<\/strong> occurs when the model adapts too closely to the training examples and performs worse on new cases. Specialization can also reduce capabilities that previously worked correctly.<\/p>\n<p>Evaluation should examine both the intended improvement and any errors introduced. Fine-tuning does not guarantee accuracy or, on its own, prevent fabricated responses, bias or the reproduction of sensitive information present in the data.<\/p>\n<h2>Differences between fine-tuning, instructions and RAG<\/h2>\n<p>These techniques act on different parts of the system and can be combined:<\/p>\n<ul>\n<li><strong>Instructions or prompting:<\/strong> guide the response through the content of the <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/prompt\">prompt<\/a>, without retraining the model.<\/li>\n<li><strong>RAG:<\/strong> retrieves information from external sources and includes it in the request&#8217;s context.<\/li>\n<li><strong>Fine-tuning:<\/strong> modifies parameters through training to adapt the model&#8217;s behavior.<\/li>\n<\/ul>\n<p>If the goal is to consult documentation that changes frequently, a <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/rag\">RAG<\/a> system allows the sources to be updated without repeating the model&#8217;s fine-tuning. If the aim is to maintain a specific behavior across many requests, evaluating fine-tuning may make sense.<\/p>\n<p><strong>Attaching documents does not automatically start training.<\/strong> Nor does a fine-tuned model become a database that faithfully stores and retrieves every piece of data used.<\/p>\n<p>Before training, it is worth checking whether clearer instructions, a defined output format or better context selection solve the need. Fine-tuning should deliver a measurable improvement, rather than being a mandatory step in developing an AI application.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What fine-tuning is: adapting AI models, transfer learning, training data and differences from prompting and RAG.<\/p>\n","protected":false},"author":33,"featured_media":79389,"template":"","encyclopedia-tag":[],"class_list":["post-71682","encyclopedia","type-encyclopedia","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia\/71682","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia"}],"about":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/types\/encyclopedia"}],"author":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/users\/33"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media\/79389"}],"wp:attachment":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media?parent=71682"}],"wp:term":[{"taxonomy":"encyclopedia-tag","embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia-tag?post=71682"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}