{"id":14553,"date":"2025-04-17T17:15:39","date_gmt":"2025-04-17T17:15:39","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/fine-tuning-ou-rag-como-escolher\/"},"modified":"2026-08-15T06:57:47","modified_gmt":"2026-08-15T06:57:47","slug":"fine-tuning-ou-rag-como-escolher","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/","title":{"rendered":"Fine-tuning ou RAG: como escolher a abordagem certa para IA"},"content":{"rendered":"\n<p>Empresas que <a href=\"https:\/\/cheesecakelabs.com\/blog\/como-integrar-ia-em-um-app\/\" id=\"13240\" target=\"_blank\" rel=\"noreferrer noopener\">integram solu\u00e7\u00f5es de IA<\/a> na opera\u00e7\u00e3o costumam esbarrar em um dilema estrat\u00e9gico: <\/p>\n\n\n\n<p>Investir em um modelo altamente especializado com fine-tuning ou apostar na flexibilidade do retrieval-augmented generation (RAG) para acessar informa\u00e7\u00e3o din\u00e2mica.<\/p>\n\n\n\n<p>Cada abordagem tem vantagens e desafios pr\u00f3prios, e escolher errado custa recurso desperdi\u00e7ado ou desempenho abaixo do necess\u00e1rio.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>O que \u00e9 fine-tuning?<\/strong><\/h2>\n\n\n\n<p>Fine-tuning \u00e9 treinar um modelo pr\u00e9-treinado em um dataset especializado para adapt\u00e1-lo a uma tarefa ou a um dom\u00ednio espec\u00edfico.<\/p>\n\n\n\n<p>Esse processo grava o conhecimento do dom\u00ednio direto nos par\u00e2metros do modelo, o que permite a ele dominar a terminologia e os padr\u00f5es de um nicho.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"669\" height=\"1200\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-669x1200.jpg\" alt=\"Diagrama explicando como funciona o fine-tuning de um modelo de IA\" class=\"wp-image-12677\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-669x1200.jpg 669w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-334x600.jpg 334w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-768x1378.jpg 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-856x1536.jpg 856w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-1141x2048.jpg 1141w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1-760x1364.jpg 760w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-1.jpg 1763w\" sizes=\"(max-width: 669px) 100vw, 669px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>O que \u00e9 RAG (Retrieval-Augmented Generation)?<\/strong><\/h2>\n\n\n\n<p>O RAG melhora os modelos recuperando documentos relevantes de uma base de conhecimento externa durante a infer\u00eancia. Isso permite que LLMs incorporem dados em tempo real ou espec\u00edficos de um dom\u00ednio sem precisar retreinar.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"778\" height=\"1200\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-778x1200.jpg\" alt=\"Diagrama explicando como funciona o RAG, Retrieval-Augmented Generation\" class=\"wp-image-12671\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-778x1200.jpg 778w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-389x600.jpg 389w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-768x1185.jpg 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-995x1536.jpg 995w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-1327x2048.jpg 1327w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1-760x1173.jpg 760w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-2-1.jpg 1763w\" sizes=\"(max-width: 778px) 100vw, 778px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Detalhes de implementa\u00e7\u00e3o t\u00e9cnica<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Implementa\u00e7\u00e3o de RAG<\/strong><\/h3>\n\n\n\n<p>O RAG funciona convertendo documentos em vector embeddings, que capturam o significado sem\u00e2ntico deles. Esses embeddings ficam armazenados em bancos vetoriais especializados como <a href=\"https:\/\/www.pinecone.io\/\">Pinecone<\/a>, <a href=\"https:\/\/weaviate.io\/\">Weaviate<\/a> ou <a href=\"https:\/\/qdrant.tech\/\">Qdrant<\/a>.<\/p>\n\n\n\n<p>Quando chega uma consulta, ela tamb\u00e9m \u00e9 convertida em embedding e usada para buscar documentos similares no banco. Os documentos recuperados entram como contexto para o LLM gerar a resposta.<\/p>\n\n\n\n<p>Os componentes principais s\u00e3o:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pipeline de processamento de documentos<\/strong>: divide os documentos em chunks de tamanho adequado<\/li>\n\n\n\n<li><strong>Modelo de embedding<\/strong>: transforma texto em vetores num\u00e9ricos (por exemplo, o <a href=\"https:\/\/openai.com\/index\/new-and-improved-embedding-model\/\" target=\"_blank\" rel=\"noreferrer noopener\">text-embedding-ada-002 da OpenAI<\/a>)<\/li>\n\n\n\n<li><strong>Banco vetorial<\/strong>: armazena os embeddings dos documentos e permite a busca sem\u00e2ntica<\/li>\n\n\n\n<li><strong>Mecanismo de recupera\u00e7\u00e3o<\/strong>: encontra os documentos relevantes por similaridade com a consulta<\/li>\n\n\n\n<li><strong>Prompt engineering<\/strong>: estrutura como o conte\u00fado recuperado \u00e9 apresentado ao LLM<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Implementa\u00e7\u00e3o de fine-tuning<\/strong><\/h3>\n\n\n\n<p>O fine-tuning tradicional atualiza todos os par\u00e2metros do modelo, o que sai caro em computa\u00e7\u00e3o. As t\u00e9cnicas mais recentes de fine-tuning com efici\u00eancia de par\u00e2metros reduzem bastante esse custo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/huggingface.co\/docs\/diffusers\/en\/training\/lora\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>LoRA (Low-Rank Adaptation)<\/strong><\/a>: treina apenas um n\u00famero pequeno de par\u00e2metros de adapta\u00e7\u00e3o e mant\u00e9m o modelo base congelado, o que reduz o custo de treinamento em at\u00e9 90% sem perder desempenho.<\/li>\n\n\n\n<li><a href=\"https:\/\/huggingface.co\/blog\/4bit-transformers-bitsandbytes\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>QLoRA<\/strong>:<\/a> combina quantiza\u00e7\u00e3o com LoRA para ganhar ainda mais efici\u00eancia, o que viabiliza fine-tuning em hardware de consumo.<\/li>\n\n\n\n<li><a href=\"https:\/\/huggingface.co\/blog\/peft\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>PEFT (Parameter-Efficient Fine-Tuning)<\/strong>:<\/a> uma fam\u00edlia de t\u00e9cnicas que inclui adapters, prefix tuning e prompt tuning.<\/li>\n<\/ul>\n\n\n\n<p>Essas abordagens tornaram o fine-tuning mais acess\u00edvel, mas ele continua exigindo dados de treino curados e conhecimento t\u00e9cnico.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Custo de tokens vs. custo de treinamento: a an\u00e1lise econ\u00f4mica<\/strong><\/h2>\n\n\n\n<p>O trade-off econ\u00f4mico entre as duas abordagens pode ser visualizado&nbsp;assim:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"369\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3-1200x369.png\" alt=\"Compara\u00e7\u00e3o entre custo de tokens e custo de treinamento em fine-tuning e RAG\" class=\"wp-image-12673\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3-1200x369.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3-600x184.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3-768x236.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3-760x234.png 760w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-3.png 1321w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Quando escolher fine-tuning e quando escolher RAG<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"820\" height=\"1200\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-820x1200.jpg\" alt=\"Quadro comparativo de quando escolher fine-tuning e quando escolher RAG\" class=\"wp-image-12675\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-820x1200.jpg 820w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-410x600.jpg 410w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-768x1124.jpg 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-1049x1536.jpg 1049w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-1399x2048.jpg 1399w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3-760x1113.jpg 760w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/FT-VS-RAG-4-3.jpg 1763w\" sizes=\"(max-width: 820px) 100vw, 820px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Est\u00e1gios da solu\u00e7\u00e3o e escolha do m\u00e9todo certo<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Prova de conceito (PoC)<\/strong>: comece com <strong>RAG<\/strong> para validar mais r\u00e1pido e com menos custo inicial.<\/li>\n\n\n\n<li><a href=\"https:\/\/cheesecakelabs.com\/blog\/mvp-meaning\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Produto m\u00ednimo vi\u00e1vel (MVP)<\/strong><\/a>: se o or\u00e7amento permitir, o <strong>fine-tuning<\/strong> entrega uma experi\u00eancia mais refinada. Caso contr\u00e1rio, <strong>RAG<\/strong> continua sendo uma escolha forte.<\/li>\n\n\n\n<li><strong>Startups<\/strong>: considere uma abordagem h\u00edbrida; comece com <strong>RAG<\/strong> e migre para <strong>fine-tuning<\/strong> conforme seus dados e seu or\u00e7amento crescem.<\/li>\n\n\n\n<li><strong>Grandes empresas<\/strong>: dependendo da necessidade, d\u00e1 para usar <strong>fine-tuning<\/strong> nas ferramentas internas e <strong>RAG<\/strong> nas aplica\u00e7\u00f5es voltadas ao cliente, que exigem informa\u00e7\u00e3o atualizada.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Abordagem h\u00edbrida<\/strong><\/h3>\n\n\n\n<p>Combinar RAG e fine-tuning parece atraente, mas costuma render menos do que se espera, porque os objetivos das duas t\u00e9cnicas conflitam. Se for tentar:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use fine-tuning para o conhecimento fundamental do dom\u00ednio.<\/li>\n\n\n\n<li>Use RAG para as atualiza\u00e7\u00f5es em tempo real.<\/li>\n\n\n\n<li>Teste com rigor: a integra\u00e7\u00e3o nem sempre \u00e9 tranquila.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Tend\u00eancias atuais e caminhos futuros<\/strong><\/h2>\n\n\n\n<p>\u00c0 medida que os modelos crescem, de bilh\u00f5es para trilh\u00f5es de par\u00e2metros, a vantagem de custo do RAG fica ainda maior.<\/p>\n\n\n\n<p>O surgimento dos modelos multimodais, que lidam com texto, imagem e \u00e1udio, complica ainda mais o fine-tuning. O RAG se adapta com mais facilidade, porque basta incorporar diferentes tipos de m\u00eddia \u00e0 base de conhecimento.<\/p>\n\n\n\n<p>Modelos open-source est\u00e3o tornando o fine-tuning mais acess\u00edvel, enquanto a tecnologia de bancos vetoriais melhora rapidamente o desempenho dos sistemas de RAG.<\/p>\n\n\n\n<p>Esses movimentos paralelos indicam que as duas abordagens v\u00e3o continuar evoluindo, cada uma com seus casos de uso espec\u00edficos.<\/p>\n\n\n\n<p><strong>Para fechar<\/strong><\/p>\n\n\n\n<p>Para as empresas, justificar o custo alto do fine-tuning, financeiro e operacional (cada atualiza\u00e7\u00e3o exige retreinar), fica cada vez mais dif\u00edcil, na medida em que RAG e prompt engineering se firmam como alternativas escal\u00e1veis e mais baratas.<\/p>\n\n\n\n<p><strong>A efici\u00eancia de custo do RAG:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>O RAG dispensa o custo inicial de treinamento e reduz o esfor\u00e7o de manuten\u00e7\u00e3o, j\u00e1 que atualizar a base de conhecimento n\u00e3o exige retreinar o modelo.<\/li>\n\n\n\n<li>Estudos de <a href=\"https:\/\/arxiv.org\/abs\/2311.05903\" target=\"_blank\" rel=\"noreferrer noopener\">Dodgson et al. (2023)<\/a> mostram que RAG combinado com prompt engineering chega a cerca de 81% de acur\u00e1cia em tarefas de recupera\u00e7\u00e3o de informa\u00e7\u00e3o din\u00e2mica, como an\u00e1lise financeira atual e eventos recentes. Na compara\u00e7\u00e3o com fine-tuning sobre datasets est\u00e1ticos, as alucina\u00e7\u00f5es caem em torno de 80%.<\/li>\n<\/ul>\n\n\n\n<p><strong>Prompt engineering como alternativa de baixo custo:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Prompts de sistema simples (por exemplo, \u201cVoc\u00ea \u00e9 um analista especialista\u2026\u201d) guiam o modelo a focar no contexto recuperado e melhoram a acur\u00e1cia sem fine-tuning.<\/li>\n\n\n\n<li>Segundo <a href=\"https:\/\/arxiv.org\/abs\/2311.05903\" target=\"_blank\" rel=\"noreferrer noopener\">Dodgson et al. (2023)<\/a>, prompts bem constru\u00eddos reduzem as alucina\u00e7\u00f5es em cerca de 10% no GPT-3.5 base, chegando perto do desempenho de modelos com fine-tuning por uma fra\u00e7\u00e3o do custo.<\/li>\n<\/ul>\n\n\n\n<p><strong>Quando o fine-tuning ainda se justifica:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dom\u00ednios altamente regulados (por exemplo, sa\u00fade e direito):<\/strong> o fine-tuning garante ader\u00eancia a uma terminologia estrita e reduz a depend\u00eancia de dados externos.<\/li>\n\n\n\n<li><strong>Aplica\u00e7\u00f5es offline: <\/strong>em sistemas isolados da rede (por exemplo, defesa e ferramentas on-premise), o fine-tuning continua essencial.<\/li>\n<\/ul>\n\n\n\n<p>Ainda assim, para a maioria dos casos de uso corporativos (atendimento ao cliente, an\u00e1lise de mercado, bases de conhecimento internas), RAG com prompt engineering entrega desempenho compar\u00e1vel ao do fine-tuning e se alinha melhor \u00e0s metas de or\u00e7amento e escalabilidade.<\/p>\n\n\n\n<p>Para quem n\u00e3o \u00e9 especialista, RAG com prompts de sistema (por exemplo, \u201cVoc\u00ea \u00e9 especialista em\u2026\u201d) oferece o melhor equil\u00edbrio entre acur\u00e1cia, custo e acessibilidade. O fine-tuning segue sendo uma ferramenta poderosa, mas de nicho, para customiza\u00e7\u00e3o profunda.<\/p>\n\n\n\n<p><strong>Refer\u00eancias:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/arxiv.org\/abs\/2403.01432\" rel=\"nofollow\">Soudani et al. (2024): Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2312.05934\" rel=\"nofollow\">Ovadia et al. (2023): Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs<\/a><\/li>\n\n\n\n<li>Lakatos et al. (2024): Investigating the Performance of Retrieval-Augmented Generation and Fine-Tuning for AI-Driven Knowledge Systems<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2311.05903\" rel=\"nofollow\">Dodgson et al. (2023): Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and System Prompting<\/a><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/cheesecakelabs.com\/br\/contact\/\"><img decoding=\"async\" width=\"1157\" height=\"506\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA.png\" alt=\"Banner da Cheesecake Labs para falar com um especialista em IA\" class=\"wp-image-12612\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA.png 1157w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-600x262.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-768x336.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-760x332.png 760w\" sizes=\"(max-width: 1157px) 100vw, 1157px\" \/><\/a><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Empresas que integram solu\u00e7\u00f5es de IA na opera\u00e7\u00e3o costumam esbarrar em um dilema estrat\u00e9gico: Investir em um modelo altamente especializado com fine-tuning ou apostar na flexibilidade do retrieval-augmented generation (RAG) para acessar informa\u00e7\u00e3o din\u00e2mica. Cada abordagem tem vantagens e desafios pr\u00f3prios, e escolher errado custa recurso desperdi\u00e7ado ou desempenho abaixo do necess\u00e1rio. O que \u00e9 [&hellip;]<\/p>\n","protected":false},"author":92,"featured_media":12663,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_title":"Fine-tuning ou RAG: como escolher a abordagem certa","_yoast_wpseo_metadesc":"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.","_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_canonical":"","ai_summary":"Fine-tuning adapta um modelo pr\u00e9-treinado a um dom\u00ednio espec\u00edfico gravando o conhecimento em seus par\u00e2metros, enquanto o RAG recupera documentos de uma base externa durante a infer\u00eancia, incorporando dados atualizados sem precisar retreinar.\nT\u00e9cnicas como LoRA, QLoRA e PEFT reduzem o custo do fine-tuning (em at\u00e9 90% no caso do LoRA), mas ele continua exigindo dados curados e conhecimento t\u00e9cnico; o RAG dispensa o custo inicial de treinamento e facilita a manuten\u00e7\u00e3o.\nA recomenda\u00e7\u00e3o varia por est\u00e1gio: RAG para PoC, fine-tuning no MVP se o or\u00e7amento permitir, abordagem h\u00edbrida para startups e, em grandes empresas, fine-tuning em ferramentas internas e RAG em aplica\u00e7\u00f5es voltadas ao cliente.\nPara a maioria dos casos corporativos, RAG com prompt engineering entrega desempenho compar\u00e1vel ao fine-tuning com menor custo, chegando a cerca de 81% de acur\u00e1cia em recupera\u00e7\u00e3o din\u00e2mica; o fine-tuning segue justificado em dom\u00ednios altamente regulados e aplica\u00e7\u00f5es offline.","ai_summary_en":"","ai_summary_pt-br":"Fine-tuning adapta um modelo pr\u00e9-treinado a um dom\u00ednio espec\u00edfico gravando o conhecimento em seus par\u00e2metros, enquanto o RAG recupera documentos de uma base externa durante a infer\u00eancia, incorporando dados atualizados sem precisar retreinar.\nT\u00e9cnicas como LoRA, QLoRA e PEFT reduzem o custo do fine-tuning (em at\u00e9 90% no caso do LoRA), mas ele continua exigindo dados curados e conhecimento t\u00e9cnico; o RAG dispensa o custo inicial de treinamento e facilita a manuten\u00e7\u00e3o.\nA recomenda\u00e7\u00e3o varia por est\u00e1gio: RAG para PoC, fine-tuning no MVP se o or\u00e7amento permitir, abordagem h\u00edbrida para startups e, em grandes empresas, fine-tuning em ferramentas internas e RAG em aplica\u00e7\u00f5es voltadas ao cliente.\nPara a maioria dos casos corporativos, RAG com prompt engineering entrega desempenho compar\u00e1vel ao fine-tuning com menor custo, chegando a cerca de 81% de acur\u00e1cia em recupera\u00e7\u00e3o din\u00e2mica; o fine-tuning segue justificado em dom\u00ednios altamente regulados e aplica\u00e7\u00f5es offline.","footnotes":"","ckl_wpml_lang":"br","ckl_wpml_source_id":12651,"ckl_wpml_status":"ok: lang=br trid=54576 source=12651 at 2026-08-14 16:56:39"},"categories":[1422],"tags":[305,1148,54,1150,1199],"class_list":["post-14553","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-implementation","tag-tag-development","tag-tag-development-br","tag-tag-mobile-app-development","tag-tag-mobile-app-development-br","tag-software-development"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Fine-tuning ou RAG: como escolher a abordagem certa<\/title>\n<meta name=\"description\" content=\"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fine-tuning ou RAG: como escolher a abordagem certa\" \/>\n<meta property=\"og:description\" content=\"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\" \/>\n<meta property=\"og:site_name\" content=\"Cheesecake Labs\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cheesecakelabs\" \/>\n<meta property=\"article:published_time\" content=\"2025-04-17T17:15:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-15T06:57:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1921\" \/>\n\t<meta property=\"og:image:height\" content=\"861\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Cheesecake Labs\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@cheesecakelabs\" \/>\n<meta name=\"twitter:site\" content=\"@cheesecakelabs\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\"},\"author\":{\"name\":\"Diana Martins\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/37fa2f0fa5f8d3cbaeb21b973fa5c4b9\"},\"headline\":\"Fine-tuning ou RAG: como escolher a abordagem certa para IA\",\"datePublished\":\"2025-04-17T17:15:39+00:00\",\"dateModified\":\"2026-08-15T06:57:47+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\"},\"wordCount\":1115,\"publisher\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#organization\"},\"image\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png\",\"keywords\":[\"development\",\"development\",\"mobile app development\",\"mobile app development\",\"software development\"],\"articleSection\":[\"AI Implementation\"],\"inLanguage\":\"pt-BR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\",\"url\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\",\"name\":\"Fine-tuning ou RAG: como escolher a abordagem certa\",\"isPartOf\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png\",\"datePublished\":\"2025-04-17T17:15:39+00:00\",\"dateModified\":\"2026-08-15T06:57:47+00:00\",\"description\":\"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.\",\"breadcrumb\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage\",\"url\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png\",\"contentUrl\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png\",\"width\":1921,\"height\":861,\"caption\":\"cover | Cheesecake Labs\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/cheesecakelabs.com\/blog\/br\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Fine-tuning ou RAG: como escolher a abordagem certa para IA\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#website\",\"url\":\"https:\/\/cheesecakelabs.com\/blog\/br\/\",\"name\":\"Cheesecake Labs\",\"description\":\"Empresa de desenvolvimento e design de aplicativos mobile &amp; web que est\u00e1 reinventando o desenvolvimento de produtos com times remotos. N\u00f3s desenvolvemos aplicativos iOS, Android e aplica\u00e7\u00f5es Web com as melhores empresas dos EUA, do Brasil e do mundo.\",\"publisher\":{\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/cheesecakelabs.com\/blog\/br\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#organization\",\"name\":\"Cheesecake Labs\",\"alternateName\":\"Cheesecake Labs Inc\",\"url\":\"https:\/\/cheesecakelabs.com\/\",\"logo\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/cheesecakelabs.com\/#logo\",\"url\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2022\/06\/cheesecake-labs-1.png\",\"contentUrl\":\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2022\/06\/cheesecake-labs-1.png\",\"caption\":\"Cheesecake Labs\",\"inLanguage\":\"br\"},\"image\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/cheesecakelabs.com\/#primary-image\",\"url\":\"https:\/\/ckl-website-v4-strapi-prod.s3.us-east-2.amazonaws.com\/ai_software_development_company_83fb512983.webp\",\"contentUrl\":\"https:\/\/ckl-website-v4-strapi-prod.s3.us-east-2.amazonaws.com\/ai_software_development_company_83fb512983.webp\",\"width\":1920,\"height\":1080,\"caption\":\"Cheesecake Labs \u2014 AI, Data & Blockchain software development services\",\"inLanguage\":\"br\"},\"sameAs\":[\"https:\/\/www.facebook.com\/cheesecakelabs\",\"https:\/\/x.com\/cheesecakelabs\",\"https:\/\/www.instagram.com\/cheesecakelabs\/\",\"https:\/\/www.linkedin.com\/company\/cheesecake-labs\/\",\"https:\/\/www.youtube.com\/channel\/UCdGEQ5AHJcmIlaOaI5fGGVA\",\"https:\/\/clutch.co\/profile\/cheesecake-labs\",\"https:\/\/www.behance.net\/cheesecakelabs\",\"https:\/\/dribbble.com\/cheesecakelabs\",\"https:\/\/www.designrush.com\/agency\/profile\/cheesecake-labs\",\"https:\/\/www.g2.com\/products\/cheesecake-labs\/reviews\"],\"description\":\"Cheesecake Labs is a software development studio that designs and builds custom digital products \u2014 web, mobile, and platforms \u2014 combining product design and high-performance engineering.\",\"foundingDate\":\"2013\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/37fa2f0fa5f8d3cbaeb21b973fa5c4b9\",\"name\":\"Diana Martins\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/d074fb633a22723051c9d98ffc13065a28f0b52bd30a4c0a832ee19820caa4ba?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/d074fb633a22723051c9d98ffc13065a28f0b52bd30a4c0a832ee19820caa4ba?s=96&d=mm&r=g\",\"caption\":\"Diana Martins\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Fine-tuning ou RAG: como escolher a abordagem certa","description":"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.","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:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/","og_locale":"pt_BR","og_type":"article","og_title":"Fine-tuning ou RAG: como escolher a abordagem certa","og_description":"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.","og_url":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/","og_site_name":"Cheesecake Labs","article_publisher":"https:\/\/www.facebook.com\/cheesecakelabs","article_published_time":"2025-04-17T17:15:39+00:00","article_modified_time":"2026-08-15T06:57:47+00:00","og_image":[{"width":1921,"height":861,"url":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png","type":"image\/png"}],"author":"Cheesecake Labs","twitter_card":"summary_large_image","twitter_creator":"@cheesecakelabs","twitter_site":"@cheesecakelabs","twitter_misc":{"Escrito por":null,"Est. tempo de leitura":"6 minutos"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#article","isPartOf":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/"},"author":{"name":"Diana Martins","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/37fa2f0fa5f8d3cbaeb21b973fa5c4b9"},"headline":"Fine-tuning ou RAG: como escolher a abordagem certa para IA","datePublished":"2025-04-17T17:15:39+00:00","dateModified":"2026-08-15T06:57:47+00:00","mainEntityOfPage":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/"},"wordCount":1115,"publisher":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#organization"},"image":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage"},"thumbnailUrl":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png","keywords":["development","development","mobile app development","mobile app development","software development"],"articleSection":["AI Implementation"],"inLanguage":"pt-BR"},{"@type":"WebPage","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/","url":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/","name":"Fine-tuning ou RAG: como escolher a abordagem certa","isPartOf":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#website"},"primaryImageOfPage":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage"},"image":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage"},"thumbnailUrl":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png","datePublished":"2025-04-17T17:15:39+00:00","dateModified":"2026-08-15T06:57:47+00:00","description":"Fine-tuning ou RAG? Entenda o que \u00e9 cada abordagem, os detalhes de implementa\u00e7\u00e3o e como escolher a certa para a sua solu\u00e7\u00e3o de IA.","breadcrumb":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/"]}]},{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#primaryimage","url":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png","contentUrl":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/04\/cover.png","width":1921,"height":861,"caption":"cover | Cheesecake Labs"},{"@type":"BreadcrumbList","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/fine-tuning-ou-rag-como-escolher\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/cheesecakelabs.com\/blog\/br\/blog\/"},{"@type":"ListItem","position":2,"name":"Fine-tuning ou RAG: como escolher a abordagem certa para IA"}]},{"@type":"WebSite","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#website","url":"https:\/\/cheesecakelabs.com\/blog\/br\/","name":"Cheesecake Labs","description":"Empresa de desenvolvimento e design de aplicativos mobile &amp; web que est\u00e1 reinventando o desenvolvimento de produtos com times remotos. N\u00f3s desenvolvemos aplicativos iOS, Android e aplica\u00e7\u00f5es Web com as melhores empresas dos EUA, do Brasil e do mundo.","publisher":{"@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/cheesecakelabs.com\/blog\/br\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#organization","name":"Cheesecake Labs","alternateName":"Cheesecake Labs Inc","url":"https:\/\/cheesecakelabs.com\/","logo":{"@type":"ImageObject","@id":"https:\/\/cheesecakelabs.com\/#logo","url":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2022\/06\/cheesecake-labs-1.png","contentUrl":"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2022\/06\/cheesecake-labs-1.png","caption":"Cheesecake Labs","inLanguage":"br"},"image":{"@type":"ImageObject","@id":"https:\/\/cheesecakelabs.com\/#primary-image","url":"https:\/\/ckl-website-v4-strapi-prod.s3.us-east-2.amazonaws.com\/ai_software_development_company_83fb512983.webp","contentUrl":"https:\/\/ckl-website-v4-strapi-prod.s3.us-east-2.amazonaws.com\/ai_software_development_company_83fb512983.webp","width":1920,"height":1080,"caption":"Cheesecake Labs \u2014 AI, Data & Blockchain software development services","inLanguage":"br"},"sameAs":["https:\/\/www.facebook.com\/cheesecakelabs","https:\/\/x.com\/cheesecakelabs","https:\/\/www.instagram.com\/cheesecakelabs\/","https:\/\/www.linkedin.com\/company\/cheesecake-labs\/","https:\/\/www.youtube.com\/channel\/UCdGEQ5AHJcmIlaOaI5fGGVA","https:\/\/clutch.co\/profile\/cheesecake-labs","https:\/\/www.behance.net\/cheesecakelabs","https:\/\/dribbble.com\/cheesecakelabs","https:\/\/www.designrush.com\/agency\/profile\/cheesecake-labs","https:\/\/www.g2.com\/products\/cheesecake-labs\/reviews"],"description":"Cheesecake Labs is a software development studio that designs and builds custom digital products \u2014 web, mobile, and platforms \u2014 combining product design and high-performance engineering.","foundingDate":"2013"},{"@type":"Person","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/37fa2f0fa5f8d3cbaeb21b973fa5c4b9","name":"Diana Martins","image":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/cheesecakelabs.com\/blog\/br\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/d074fb633a22723051c9d98ffc13065a28f0b52bd30a4c0a832ee19820caa4ba?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/d074fb633a22723051c9d98ffc13065a28f0b52bd30a4c0a832ee19820caa4ba?s=96&d=mm&r=g","caption":"Diana Martins"}}]}},"_links":{"self":[{"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/posts\/14553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/users\/92"}],"replies":[{"embeddable":true,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/comments?post=14553"}],"version-history":[{"count":3,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/posts\/14553\/revisions"}],"predecessor-version":[{"id":15042,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/posts\/14553\/revisions\/15042"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/media\/12663"}],"wp:attachment":[{"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/media?parent=14553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/categories?post=14553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cheesecakelabs.com\/blog\/br\/wp-json\/wp\/v2\/tags?post=14553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}