{"id":35,"date":"2026-03-05T16:50:50","date_gmt":"2026-03-05T16:50:50","guid":{"rendered":"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/"},"modified":"2026-03-18T22:00:08","modified_gmt":"2026-03-18T22:00:08","slug":"rag-vs-fine-tuning-when-to-use-each-technique-for","status":"publish","type":"post","link":"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/","title":{"rendered":"RAG vs Fine-Tuning: What I Actually Learned After 6 Months of Building LLM Apps"},"content":{"rendered":"<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"RAG <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning\/\" title=\"vs <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning\/\" title=\"Fine-Tuning: What\">Fine-Tuning: What<\/a> I\">vs <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning\/\" title=\"Fine-Tuning: What\">Fine-Tuning: What<\/a> I<\/a> Actually <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning\/\" title=\"Learned After\">Learned After<\/a> 6 <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/deno-20-in-production-2026-migration-from-nodejs-a\/\" title=\"Months of\">Months of<\/a> Building LLM Apps\",\n  \"description\": \"Six months ago my team was building an internal support tool for a B2B SaaS company \u2014 about 120 employees, docs spread across Notion, Confluence, and a hal\",\n  \"url\": \"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/\",\n  \"datePublished\": \"2026-03-05T16:50:50\",\n  \"dateModified\": \"2026-03-05T17:39:32\",\n  \"inLanguage\": \"en_US\",\n  \"author\": {\n    \"@type\": \"Organization\",\n    \"name\": \"RebalAI\",\n    \"url\": \"https:\/\/blog.rebalai.com\/en\/\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"RebalAI\",\n    \"logo\": {\n      \"@type\": \"ImageObject\",\n      \"url\": \"https:\/\/blog.rebalai.com\/wp-content\/uploads\/logo.png\"\n    }\n  },\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/\"\n  }\n}\n<\/script><\/p>\n<p>Six months ago my team was building an internal support tool for a B2B SaaS company \u2014 about 120 employees, docs spread across Notion, Confluence, and a half-dead SharePoint instance from 2019. The ask was simple: a chatbot that could answer questions about internal processes without making stuff up.<\/p>\n<p>Simple, right.<\/p>\n<p>I had to make the call: RAG or fine-tune a model. I&#8217;d read the think pieces. I&#8217;d watched the YouTube explainers. None of them gave me the answer <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/github-copilot-alternatives-in-2026-cursor-codeium\/\" title=\"I Actually\">I actually<\/a> needed, which was <em>which one for this specific situation, <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/deno-20-in-production-2026-migration-from-nodejs-a\/\" title=\"and What\">and what<\/a> will break first.<\/em> So I spent about <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/deno-20-in-production-2026-migration-from-nodejs-a\/\" title=\"Six Months\">six months<\/a> <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/docker-compose-vs-kubernetes-when-to-use-which-in\/\" title=\"Running Both\">running both<\/a> approaches across three different projects, and here&#8217;s <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/github-copilot-alternatives-in-2026-cursor-codeium\/\" title=\"What I Actually\">what I actually<\/a> found.<\/p>\n<hr \/>\n<h2>Why Most Comparisons Miss the Point<\/h2>\n<p>The framing of &#8220;<a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning\/\" title=\"RAG vs\">RAG vs<\/a> fine-tuning&#8221; is a bit of a false dichotomy, but before I get to that \u2014 the techniques solve genuinely different problems, and conflating them leads to expensive mistakes.<\/p>\n<p>Here is the thing: fine-tuning changes <em>how<\/em> a model thinks. RAG changes <em>what<\/em> a model knows at query time. That distinction sounds obvious written out, but in practice it&#8217;s easy to reach for fine-tuning when you actually need RAG, because fine-tuning feels more &#8220;serious.&#8221; More ML-ish. More like you&#8217;re doing real AI work.<\/p>\n<p>I made that mistake on my first project. More on that in a bit.<\/p>\n<p>RAG \u2014 retrieval-augmented generation \u2014 keeps the base model frozen and instead pulls relevant chunks of text into the prompt at inference time. Your vector database stores embeddings of your documents; at query time you embed the user&#8217;s question, find the nearest neighbors, and stuff them into context. The model never &#8220;learns&#8221; your data \u2014 it just reads it fresh every time.<\/p>\n<p>Fine-tuning takes a pre-trained model and continues training it on your dataset. The weights change. The model bakes your domain knowledge into its parameters. It <em>becomes<\/em> a different model.<\/p>\n<p>Both have their place. The problem is figuring out which place that is.<\/p>\n<hr \/>\n<h2>Where RAG Actually Shines (And It&#8217;s Not Just &#8220;Knowledge Updates&#8221;)<\/h2>\n<p>Most articles will <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/setting-up-github-actions-for-python-applications\/\" title=\"Tell You\">tell you<\/a> <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/claude-vs-gpt-4o-vs-gemini-20-which-ai-model-to-us\/\" title=\"to Use\">to use<\/a> RAG when your data changes frequently. That&#8217;s true, but it undersells the technique. RAG shines in a few other scenarios that I didn&#8217;t fully appreciate until I was deep <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> weeds.<\/p>\n<p><strong>When your source of truth is authoritative and you need citations.<\/strong> The internal support tool I mentioned \u2014 legal and HR docs, policy PDFs, process guides \u2014 RAG was almost the only sensible answer. Users needed to know <em>where<\/em> the answer came from, not just <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/setting-up-github-actions-for-python-applications\/\" title=\"What the\">what the<\/a> answer was. With RAG you can return source chunks alongside the response. With fine-tuning, the model just&#8230; says things. Confidently. With no provenance.<\/p>\n<p><strong>When your corpus is large and heterogeneous.<\/strong> Fine-<a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/postgresql-performance-tuning-what-i-learned-optim\/\" title=\"Tuning on\">tuning on<\/a> 10,000 Confluence pages would require careful curation, cleaning, formatting into training examples, and a training run that costs real money. With RAG, I ingested everything into a Chroma instance in a few hours and had a working prototype by end of day.<\/p>\n<p><strong>When you can&#8217;t afford to be wrong about freshness.<\/strong> Fine-tuned models go stale. If your pricing changes or your API specs update, a fine-tuned model will confidently give old information. A RAG system \u2014 if your ingestion pipeline is solid \u2014 serves fresh data.<\/p>\n<p>Here&#8217;s a simplified version of the ingestion + query loop I used on that project (this was with LangChain 0.2.x, which had actually cleaned up the API considerably from the 0.1 chaos):<\/p>\n<pre><code class=\"language-python\">from langchain_community.vectorstores import Chroma\nfrom langchain_openai import OpenAIEmbeddings, ChatOpenAI\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.chains import RetrievalQA\n\nembeddings = OpenAIEmbeddings(model=&quot;text-embedding-3-small&quot;)\n\n# Chunking strategy matters more than people think.\n# 512 tokens with 64 overlap worked well <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/edge-computing-in-2026-why-developers-are-adopting\/\" title=\"for Our\">for our<\/a> Confluence-style docs.\nsplitter = RecursiveCharacterTextSplitter(\n    chunk_size=512,\n    chunk_overlap=64,\n    separators=[&quot;\\n\\n&quot;, &quot;\\n&quot;, &quot;.&quot;, &quot; &quot;]\n)\n\ndocs = splitter.split_documents(raw_docs)\nvectorstore = Chroma.from_documents(docs, embeddings, persist_directory=&quot;.\/chroma_db&quot;)\n\n# At query time\nretriever = vectorstore.as_retriever(\n    search_type=&quot;mmr&quot;,          # maximal marginal relevance \u2014 reduces redundant chunks\n    search_kwargs={&quot;k&quot;: 6}\n)\n\nqa_chain = RetrievalQA.from_chain_type(\n    llm=ChatOpenAI(model=&quot;gpt-4o&quot;, temperature=0),\n    retriever=retriever,\n    return_source_documents=True  # this is the killer feature for trust\n)\n\nresult = qa_chain.invoke({&quot;query&quot;: &quot;What's the policy on remote work expenses?&quot;})\nprint(result[&quot;result&quot;])\nprint([doc.metadata[&quot;source&quot;] for doc in result[&quot;source_documents&quot;]])\n<\/code><\/pre>\n<p>One thing I noticed: the chunk size and overlap parameters had way more impact on quality than I expected. I spent probably three days tuning those alone. Too small and the model lacks context; too large and you&#8217;re burning tokens on irrelevant text and the retrieval precision tanks. Your mileage may vary \u2014 it depends heavily on your document structure.<\/p>\n<p><strong>The practical takeaway:<\/strong> If your problem is &#8220;the model doesn&#8217;t know my data,&#8221; try RAG first. It&#8217;s faster to iterate, cheaper to run at prototype stage, and gives you provenance for free.<\/p>\n<hr \/>\n<h2>Fine-Tuning: When the Pain <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/copilot-vs-cursor-vs-codeium\/\" title=\"Is Actually Worth\">Is Actually Worth<\/a> It<\/h2>\n<p>Fine-tuning has a deserved reputation for being annoying to get right. Dataset curation, training runs, eval frameworks, versioning model checkpoints \u2014 it&#8217;s a lot. So when is it <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/copilot-vs-cursor-vs-codeium\/\" title=\"Worth It\">worth it<\/a>?<\/p>\n<p>Honestly, the answer I&#8217;ve landed on is narrower than most people think: fine-tune when you need to change <em>behavior<\/em>, not just knowledge.<\/p>\n<p>A few cases where I&#8217;ve seen it work well:<\/p>\n<p><strong>Consistent output format.<\/strong> If your application needs the <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/claude-vs-gpt-4o-vs-gemini-20-which-ai-model-to-us\/\" title=\"Model to\">model to<\/a> always return structured JSON in a very specific schema \u2014 and <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/advanced-prompt-engineering-techniques-chain-of-th\/\" title=\"Prompt Engineering\">prompt engineering<\/a> alone keeps slipping \u2014 fine-<a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/postgresql-performance-tuning-what-i-learned-optim\/\" title=\"Tuning on\">tuning on<\/a> examples of correct behavior is surprisingly effective. I worked on a data extraction pipeline where we needed the <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/claude-vs-gpt-4o-vs-gemini-20-which-ai-model-to-us\/\" title=\"Model to\">model to<\/a> extract entities from unstructured text in a precise schema. <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"After Two Weeks of\">After two weeks of<\/a> <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/advanced-prompt-engineering-techniques-chain-of-th\/\" title=\"Prompt Engineering\">prompt engineering<\/a> gymnastics, a fine-tune on ~800 labeled examples fixed <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/copilot-vs-cursor-vs-codeium\/\" title=\"It in\">it in<\/a> one training run.<\/p>\n<p><strong>Domain-specific tone and terminology.<\/strong> A medical or legal application where specific phrasing matters, where &#8220;patient&#8221; vs &#8220;client&#8221; vs &#8220;subject&#8221; carries meaning. Fine-<a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/postgresql-performance-tuning-what-i-learned-optim\/\" title=\"Tuning on\">tuning on<\/a> domain-specific text can bake <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> right register in a way that&#8217;s hard to reliably achieve via prompting.<\/p>\n<p><strong>Latency and cost at scale.<\/strong> This one surprised me. A fine-tuned smaller model (say, GPT-4o mini on a specific task) can outperform a larger general model on that task while costing a fraction of the price. If you&#8217;re doing millions of inferences a month on a well-defined task, the economics shift significantly.<\/p>\n<p>Here&#8217;s a stripped-down example of what a fine-tuning dataset entry looks like for OpenAI&#8217;s API \u2014 the format has been stable since late 2023:<\/p>\n<pre><code class=\"language-jsonl\">{\n  &quot;messages&quot;: [\n    {\n      &quot;role&quot;: &quot;system&quot;,\n      &quot;content&quot;: &quot;You are a data extraction assistant. Extract entities and return valid JSON only.&quot;\n    },\n    {\n      &quot;role&quot;: &quot;user&quot;,\n      &quot;content&quot;: &quot;Contract signed by Meridian Holdings LLC on 2025-11-14 for $240,000 annual service.&quot;\n    },\n    {\n      &quot;role&quot;: &quot;assistant&quot;,\n      &quot;content&quot;: &quot;{\\&quot;party\\&quot;: \\&quot;Meridian Holdings LLC\\&quot;, \\&quot;date\\&quot;: \\&quot;2025-11-14\\&quot;, \\&quot;value\\&quot;: 240000, \\&quot;currency\\&quot;: \\&quot;USD\\&quot;, \\&quot;term\\&quot;: \\&quot;annual\\&quot;}&quot;\n    }\n  ]\n}\n<\/code><\/pre>\n<p>You need this format repeated hundreds to thousands of times with varied examples. The curation process is tedious. I&#8217;m not going to pretend otherwise.<\/p>\n<p><strong>The practical takeaway:<\/strong> Fine-tune when the model&#8217;s <em>behavior<\/em> is wrong, not when its <em>knowledge<\/em> is lacking. If you catch yourself writing thousand-word system prompts to control output format, that&#8217;s usually a signal that fine-tuning would clean things up.<\/p>\n<hr \/>\n<h2>The Mistake That Cost Me Two Weeks<\/h2>\n<p>So \u2014 the mistake I promised. This is the part I wish someone had told me.<\/p>\n<p>On my first LLM project (internal documentation assistant, different company, late 2024), I convinced myself we needed to fine-tune. The reasoning seemed solid: we had proprietary terminology, a specific tone of voice, and hundreds of internal documents. I spent <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"Two Weeks\">two weeks<\/a> building training data, ran a fine-tune on gpt-4o-mini, and&#8230; it was worse than the base model with a decent system prompt.<\/p>\n<p>The problem: I had confused &#8220;the model doesn&#8217;t know our docs&#8221; with &#8220;the model behaves wrong.&#8221; Those are different problems. Fine-tuning injects style and behavior patterns from your training examples. It doesn&#8217;t inject factual document content reliably. I trained it on our documents formatted as Q&amp;A pairs, and the model learned to <em>sound<\/em> like it knew things, while actually hallucinating details it didn&#8217;t retain from training.<\/p>\n<p>What I should have done was RAG, immediately, for the knowledge problem \u2014 and maybe a thin layer of fine-tuning later if the tone was still off. Instead I spent <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"Two Weeks\">two weeks<\/a> and a non-trivial API bill going the wrong direction.<\/p>\n<p>The tell? When I tested the fine-tuned model on questions about specific internal processes, it answered confidently but incorrectly about 30% of the time. The base model with RAG got the same questions right about 85% of the time, because it was reading the actual document.<\/p>\n<p>Fine-tuning does not reliably make models memorize facts from your training data. That&#8217;s what RAG is for. This is probably the single most important thing to understand about the two techniques.<\/p>\n<hr \/>\n<h2>What I&#8217;d Actually Do, Given a Choice<\/h2>\n<p>Here&#8217;s my honest recommendation, not hedged with &#8220;it depends&#8221; because that&#8217;s a non-answer.<\/p>\n<p><strong>Start with RAG, almost always.<\/strong> It&#8217;s faster, it&#8217;s more auditable, it handles data freshness gracefully, and it&#8217;s easier to debug. When a RAG response is wrong, you can look at which chunks got retrieved and understand why. When a fine-tuned model is wrong, good luck unpacking that.<\/p>\n<p><strong>Add fine-tuning if \u2014 and only if \u2014 you have a specific behavioral problem.<\/strong> Inconsistent output format, wrong tone, poor performance on a narrow well-defined task. And make sure you have at least a few hundred high-quality training examples before you start, or you&#8217;re wasting a training run.<\/p>\n<p><strong>Consider both together.<\/strong> This <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/copilot-vs-cursor-vs-codeium\/\" title=\"Is Actually\">is actually<\/a> where things get interesting. A fine-tuned model that&#8217;s better at structured output <em>plus<\/em> RAG for knowledge retrieval can be a legitimately powerful combination. For the entity extraction pipeline I mentioned, we eventually combined a fine-tuned extraction model with a small RAG component that retrieved entity type definitions. The combination outperformed either approach alone by a meaningful margin.<\/p>\n<p>I&#8217;m not 100% sure this combination scales elegantly beyond the mid-sized corpus we were working with \u2014 I&#8217;d want to see more data before recommending it universally. But on our use case (a few thousand documents, a specific extraction schema, ~50k inferences per month), it was worth the added complexity.<\/p>\n<p>One more thing: whatever <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/04\/langchain-vs-crewai-vs-anythingllm-2026\/\" title=\"You Choose\">you choose<\/a>, invest in your evaluation setup before you invest in your technique. If you can&#8217;t measure whether the model is right, you can&#8217;t know if your approach is working. I use a small golden dataset \u2014 50-100 questions with verified correct answers \u2014 that I run against every new approach. It&#8217;s not glamorous. It&#8217;s probably the most valuable 20 hours I&#8217;ve spent on any of these projects.<\/p>\n<p>The field is moving fast, and a lot of the received wisdom from 2023 is already outdated. But the fundamental question \u2014 does your problem need updated <em>knowledge<\/em> or changed <em>behavior<\/em> \u2014 that one&#8217;s stayed stable. Start there.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>{ &#8220;@context&#8221;: &#8220;https:\/\/schema.org&#8221;, &#8220;@type&#8221;: &#8220;BlogPosting&#8221;, &#8220;headline&#8221;: &#8220;RAG Fine-Tuning: What I&#8221;>vs Fine-Tuning: What I Actually Learned After 6 Months of<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","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":[1],"tags":[],"class_list":["post-35","post","type-post","status-publish","format-standard","hentry","category-general"],"_links":{"self":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/35","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/comments?post=35"}],"version-history":[{"count":16,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/35\/revisions"}],"predecessor-version":[{"id":535,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/35\/revisions\/535"}],"wp:attachment":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/media?parent=35"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/categories?post=35"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/tags?post=35"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}