{"id":55,"date":"2026-05-18T12:37:27","date_gmt":"2026-05-18T12:37:27","guid":{"rendered":"https:\/\/convly.ai\/fine-tuning-vs-rag\/"},"modified":"2026-06-10T05:06:08","modified_gmt":"2026-06-10T05:06:08","slug":"fine-tuning-vs-rag","status":"publish","type":"post","link":"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/","title":{"rendered":"Fine-Tuning vs RAG in 2026: When to Use Each (and When to Use Both)"},"content":{"rendered":"<p>When teams want a language model to do something specific \u2014 answer from their data, speak in their voice, perform their task \u2014 they reach a fork in the road: <strong>fine-tuning<\/strong> or <strong>RAG<\/strong>. The two are often presented as competitors, but that framing causes most of the confusion. They solve <em>different<\/em> problems. Choosing well starts with understanding which problem you actually have.<\/p>\n<p>This guide explains both clearly, compares their costs and trade-offs, and gives you a decision framework.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principais conclus\u00f5es<\/h3>\n<ul>\n<li><strong>RAG adds knowledge.<\/strong> It gives the model access to information at question time.<\/li>\n<li><strong>Fine-tuning changes behavior.<\/strong> It teaches the model a style, format, or task.<\/li>\n<li><strong>The test:<\/strong> &#8220;The model doesn&#8217;t <em>know<\/em> something&#8221; \u2192 RAG. &#8220;The model doesn&#8217;t <em>act<\/em> the way I need&#8221; \u2192 fine-tuning.<\/li>\n<li><strong>Start with RAG.<\/strong> It&#8217;s cheaper, faster, easier to update, and solves the most common need.<\/li>\n<li><strong>Combine them<\/strong> for the hardest cases: fine-tune for behavior, add RAG for knowledge.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a38af0ed9b7e\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a38af0ed9b7e\"  aria-label=\"Alternar\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#What_each_one_actually_does\" >What each one actually does<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#The_key_distinction\" >The key distinction<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#Side-by-side_comparison\" >Side-by-side comparison<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#Why_you_should_usually_start_with_RAG\" >Why you should usually start with RAG<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#When_fine-tuning_is_the_right_call\" >When fine-tuning is the right call<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#When_to_use_both\" >When to use both<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#A_decision_framework_climb_the_cheapest_rung_first\" >A decision framework: climb the cheapest rung first<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#FAQ\" >Perguntas frequentes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#Bottom_line\" >Conclus\u00e3o<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/pt\/fine-tuning-vs-rag\/#Related_articles\" >Artigos relacionados<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_each_one_actually_does\"><\/span>What each one actually does<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>RAG: giving the model knowledge<\/h3>\n<p><a href=\"\/pt\/rag-retrieval-augmented-generation-explained\/\">Retrieval-augmented generation<\/a> keeps your information in an external knowledge base. At question time, it retrieves the relevant passages and inserts them into the prompt, so the model answers from supplied facts rather than memory. The model itself is never changed \u2014 you&#8217;re changing what it <em>sees<\/em>.<\/p>\n<p>RAG is the answer when the model needs <strong>information it doesn&#8217;t have<\/strong>: your documentation, your product catalog, your policies, current data.<\/p>\n<h3>Fine-tuning: changing the model&#8217;s behavior<\/h3>\n<p>Fine-tuning continues training a base model on a set of your own examples. It adjusts the model&#8217;s actual weights, shifting how it responds. After fine-tuning, the model has internalized a pattern \u2014 a tone, a format, a way of performing a specific task.<\/p>\n<p>Fine-tuning is the answer when the model needs to <strong>behave differently<\/strong>: always reply in a precise JSON schema, consistently adopt a brand voice, or handle a specialized task in a particular way.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_key_distinction\"><\/span>The key distinction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here is the test that resolves most decisions:<\/p>\n<blockquote>\n<p><strong>If the problem is &#8220;the model doesn&#8217;t <em>know<\/em> X&#8221; \u2192 you need RAG.<\/strong><br \/>\n<strong>If the problem is &#8220;the model doesn&#8217;t <em>act<\/em> the way I need&#8221; \u2192 you need fine-tuning.<\/strong><\/p>\n<\/blockquote>\n<p>A support bot that needs to answer from your help center has a <em>knowledge<\/em> problem \u2192 RAG. A model that should always output data in your exact format, or always write in your company&#8217;s distinctive style, has a <em>behavior<\/em> problem \u2192 fine-tuning. A customer-service AI that needs both your policies <em>e<\/em> a consistent on-brand tone has both \u2192 combine them.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Side-by-side_comparison\"><\/span>Side-by-side comparison<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Factor<\/th>\n<th>RAG<\/th>\n<th>Fine-tuning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Solves<\/td>\n<td>Missing knowledge<\/td>\n<td>Wrong behavior \/ style \/ format<\/td>\n<\/tr>\n<tr>\n<td>Changes the model?<\/td>\n<td>No \u2014 changes the prompt<\/td>\n<td>Yes \u2014 changes the weights<\/td>\n<\/tr>\n<tr>\n<td>Updating information<\/td>\n<td>Instant \u2014 edit the knowledge base<\/td>\n<td>Requires retraining<\/td>\n<\/tr>\n<tr>\n<td>Upfront cost &amp; effort<\/td>\n<td>Lower<\/td>\n<td>Higher (data prep + training)<\/td>\n<\/tr>\n<tr>\n<td>Per-request cost<\/td>\n<td>Higher (longer prompts)<\/td>\n<td>Lower (shorter prompts)<\/td>\n<\/tr>\n<tr>\n<td>Reduces hallucination<\/td>\n<td>Yes, strongly<\/td>\n<td>Not directly<\/td>\n<\/tr>\n<tr>\n<td>Source citations<\/td>\n<td>Yes \u2014 you know what was retrieved<\/td>\n<td>N\u00e3o<\/td>\n<\/tr>\n<tr>\n<td>Ideal para<\/td>\n<td>Q&amp;A over documents, current data<\/td>\n<td>Consistent format, voice, niche tasks<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Why_you_should_usually_start_with_RAG\"><\/span>Why you should usually start with RAG<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For most projects, RAG is the right first move:<\/p>\n<ul>\n<li><strong>It solves the most common need<\/strong> \u2014 the majority of &#8220;customize the model&#8221; requests are really &#8220;make it answer from our data.&#8221;<\/li>\n<li><strong>It&#8217;s cheaper and faster to build<\/strong> \u2014 no training run, no labeled dataset.<\/li>\n<li><strong>It updates instantly<\/strong> \u2014 change a document and the system reflects it immediately; no retraining cycle.<\/li>\n<li><strong>It cuts hallucination and gives citations<\/strong> \u2014 answers are grounded and traceable.<\/li>\n<li><strong>It&#8217;s easier to debug<\/strong> \u2014 you can inspect exactly which passages were retrieved.<\/li>\n<\/ul>\n<p>Fine-tuning&#8217;s classic failure mode is teams using it to inject knowledge. It works poorly for that: facts learned through fine-tuning are fuzzy, hard to update, and the model may still hallucinate around them. Don&#8217;t fine-tune to <em>add facts<\/em> \u2014 fine-tune to <em>change behavior<\/em>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"When_fine-tuning_is_the_right_call\"><\/span>When fine-tuning is the right call<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Reach for fine-tuning when:<\/p>\n<ul>\n<li>Voc\u00ea precisa <strong>strict, consistent output format<\/strong> every time (a fixed JSON schema, a specific structure).<\/li>\n<li>You need a <strong>distinctive, consistent voice or style<\/strong> that prompting can&#8217;t reliably hold.<\/li>\n<li>You have a <strong>narrow, repetitive task<\/strong> the base model does adequately but not reliably enough.<\/li>\n<li>You want to <strong>shorten prompts and cut latency<\/strong> \u2014 a fine-tuned model needs fewer instructions and examples per request, which lowers cost at high volume.<\/li>\n<li>Prompt engineering has genuinely hit its ceiling for your task.<\/li>\n<\/ul>\n<p>A practical note: always exhaust good prompting and few-shot examples <em>first<\/em>. Modern models are so capable that many problems people reach for fine-tuning to solve can be handled with a well-built prompt.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"When_to_use_both\"><\/span>When to use both<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The most demanding production systems combine the two. Fine-tune the model so it reliably behaves the way you need \u2014 correct tone, correct format, correct task handling \u2014 and add RAG so it always has the right, current knowledge to work with.<\/p>\n<p>Example: a customer-support assistant. Fine-tune it to respond in your brand voice and always follow your support structure (behavior); use RAG to feed it the latest help-center articles and the specific customer&#8217;s account context (knowledge). Behavior from fine-tuning, facts from RAG \u2014 each doing the job it&#8217;s actually good at.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"A_decision_framework_climb_the_cheapest_rung_first\"><\/span>A decision framework: climb the cheapest rung first<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The fastest way to waste a month is to reach for fine-tuning before you have exhausted the cheaper options. In practice the choices form a ladder, ordered from least to most effort, cost, and maintenance. The expert consensus in 2026 is blunt: <strong>start at the bottom and only climb when the rung below genuinely cannot do the job.<\/strong><\/p>\n<ul>\n<li><strong>Rung 1 \u2014 A better prompt (and a bigger context window).<\/strong> Before any infrastructure, improve the instructions and paste the relevant material straight into the prompt. Frontier models now accept context windows ranging from hundreds of thousands to well over a million tokens, so if your knowledge is small and fairly static, you may not need a retrieval system at all. This costs nothing but an afternoon.<\/li>\n<li><strong>Rung 2 \u2014 RAG.<\/strong> Move up only when your knowledge is too large to paste, changes frequently, or needs source citations. RAG adds a retrieval pipeline and latency, but it keeps answers current and auditable.<\/li>\n<li><strong>Rung 3 \u2014 Fine-tuning.<\/strong> Reserve this for changing <em>behaviour<\/em>: a fixed output format, a specialised tone, a narrow classification task, or a skill the base model performs unreliably no matter how you prompt it.<\/li>\n<\/ul>\n<p>The trade-offs at a glance:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th scope=\"col\">Dimens\u00e3o<\/th>\n<th scope=\"col\">Better prompt<\/th>\n<th scope=\"col\">RAG<\/th>\n<th scope=\"col\">Fine-tuning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Effort to ship<\/td>\n<td>Hours<\/td>\n<td>Days to weeks<\/td>\n<td>Weeks (plus data work)<\/td>\n<\/tr>\n<tr>\n<td>Upfront cost<\/td>\n<td>Near zero<\/td>\n<td>Moderate (vector store, pipeline)<\/td>\n<td>Higher (curated dataset + GPU time)<\/td>\n<\/tr>\n<tr>\n<td>Keeps facts current<\/td>\n<td>Manual<\/td>\n<td>Yes, re-index to update<\/td>\n<td>No \u2014 frozen at training time<\/td>\n<\/tr>\n<tr>\n<td>Melhor em<\/td>\n<td>Quick wins, small\/static data<\/td>\n<td>Fresh knowledge, citations<\/td>\n<td>Behaviour, format, narrow skills<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>A simple rule covers most cases. Ask first: <strong>is the problem that the model lacks <em>information<\/em>, or that it lacks the right <em>behaviour<\/em>?<\/strong> Missing information almost always points to a better prompt or RAG. Wrong behaviour \u2014 the model knows the facts but won&#8217;t structure, phrase, or classify them the way you need \u2014 is the signal to fine-tune. If you genuinely have both problems, the mature pattern is to combine them: fine-tune the behaviour once, then feed live facts through RAG at query time. Resist the temptation to skip rungs; teams that fine-tune first usually discover, expensively, that a sharper prompt or a retrieval step would have solved it in a fraction of the time.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Perguntas frequentes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is the difference between fine-tuning and RAG?<\/h3>\n<p>RAG adds knowledge to a model by retrieving relevant documents at question time, without changing the model. Fine-tuning changes the model&#8217;s behavior by further training it on examples. RAG is for missing information; fine-tuning is for changing how the model responds.<\/p>\n<h3>Should I use RAG or fine-tuning?<\/h3>\n<p>Start with RAG if the model needs information it doesn&#8217;t have \u2014 that&#8217;s the most common case, and RAG is cheaper, faster, and easy to update. Choose fine-tuning if the model needs to behave differently: a strict output format, a consistent voice, or a specialized task. For complex systems, use both.<\/p>\n<h3>Can fine-tuning add knowledge to a model?<\/h3>\n<p>Not well. Fine-tuning can nudge a model toward some information, but facts learned this way are imprecise, hard to update, and don&#8217;t reliably prevent hallucination. To give a model knowledge, use RAG. Use fine-tuning to change behavior, not to inject facts.<\/p>\n<h3>Is RAG or fine-tuning cheaper?<\/h3>\n<p>RAG is usually cheaper and easier to set up \u2014 no training run and no labeled dataset. However, RAG makes each request more expensive because it adds retrieved text to the prompt. Fine-tuning costs more upfront but can reduce per-request cost by allowing shorter prompts. At very high volume, fine-tuning can win on total cost.<\/p>\n<h3>Do RAG and fine-tuning work together?<\/h3>\n<p>Yes, and the best production systems often combine them. Fine-tune the model for consistent behavior (voice, format, task), and use RAG to supply current, specific knowledge. Each technique handles the part it&#8217;s genuinely good at.<\/p>\n<h3>How many examples do I need to fine-tune a model?<\/h3>\n<p>Fewer than most people expect, but quality matters far more than volume. For straightforward tasks such as classification, extraction, or enforcing an output format, a few hundred clean, well-labelled examples are often enough with a parameter-efficient method like LoRA. More open-ended generation or nuanced domain work pushes you toward the low thousands. Across the board, a small set of expert-validated examples beats a large pile of noisy ones, so invest your time in curating the data rather than collecting more of it.<\/p>\n<h3>Can a bigger context window replace RAG?<\/h3>\n<p>Sometimes, and that is genuinely new in 2026. If your entire knowledge base fits in the model&#8217;s context window and doesn&#8217;t change often, pasting it into the prompt can be simpler and cheaper than building a retrieval pipeline. But it breaks down at scale: long contexts cost more per call, add latency, and suffer a &#8220;lost in the middle&#8221; effect where models reliably miss facts buried between the start and end. For large, frequently updated, or citation-critical knowledge, RAG still wins.<\/p>\n<h3>Why does my RAG system give wrong or outdated answers?<\/h3>\n<p>Most RAG failures are retrieval problems, not model problems \u2014 the large majority trace back to how documents are ingested and chunked rather than to the LLM itself. The usual culprits are chunks split too aggressively (so they lose context) or too coarsely (so they match nothing well), retrieving too few passages for multi-step questions, and stale embeddings: when a source document changes but the index isn&#8217;t rebuilt, the system confidently serves the old answer. Fix the ingestion layer and re-index on a schedule before you blame or swap the model.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclus\u00e3o<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Fine-tuning and RAG aren&#8217;t rivals \u2014 they&#8217;re tools for different jobs. <strong>RAG gives a model knowledge; fine-tuning changes its behavior.<\/strong> Diagnose your problem with one question: does the model fail because it <em>doesn&#8217;t know<\/em> something, or because it <em>doesn&#8217;t act<\/em> the way you need?<\/p>\n<p>For most teams, the path is clear: start with RAG, because most customization needs are really knowledge needs, and RAG is cheaper, faster, and easier to maintain. Add fine-tuning when behavior \u2014 format, voice, a niche task \u2014 is the real gap. And for the hardest systems, combine them: fine-tuned behavior, RAG-supplied knowledge.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Artigos relacionados<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/pt\/claude-5-new-ai-models-june-2026\/\">Existe um Claude 5? Claude Fable 5 e todos os principais modelos de IA de junho de 2026<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/llm-hallucinations-complete-guide\/\">Alucina\u00e7\u00f5es de LLMs em 2026: Por que ocorrem e como evit\u00e1-las<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/prompt-engineering-techniques\/\">Engenharia de Prompt em 2026: 12 T\u00e9cnicas que Realmente Funcionam<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/what-is-ollama-complete-guide-2026\/\">O que \u00e9 Ollama? Guia completo para executar LLMs localmente em 2026<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Fine-tuning and RAG are the two ways to customize a language model \u2014 and they solve different problems. This guide gives you a clear framework for choosing the right one.<\/p>","protected":false},"author":0,"featured_media":56,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[3],"tags":[431,75,428,430,429],"class_list":["post-55","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llms","tag-ai-architecture","tag-fine-tuning-vs-rag","tag-fine-tuning","tag-llm-customization","tag-rag"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=55"}],"version-history":[{"count":3,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/55\/revisions"}],"predecessor-version":[{"id":1044,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/55\/revisions\/1044"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media\/56"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=55"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/categories?post=55"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/tags?post=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}