--- title: AutoRAG: Optimizing RAG for small models | daily.dev description: RAG pipelines have many tunable parameters—chunk size, overlap, top-k, retrieval method—and picking them by intuition leads to poor results, especially with... canonical: https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AutoRAG: Optimizing RAG for small models | daily.dev og:description: RAG pipelines have many tunable parameters—chunk size, overlap, top-k, retrieval method—and picking them by intuition leads to poor results, especially with... og:url: https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd og:image: https://api.daily.dev/og/posts/XdMZvdGCd.png og:image:alt: AutoRAG: Optimizing RAG for small models og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AutoRAG: Optimizing RAG for small models **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 10 min read · 0 upvotes · 0 comments ## Summary RAG pipelines have many tunable parameters—chunk size, overlap, top-k, retrieval method—and picking them by intuition leads to poor results, especially with small models. AutoRAG addresses this by running fast, deterministic retrieval sweeps across many configuration combinations, scoring each without any LLM calls using context recall, MRR, and context word count. A demo using a fictional bank card dispute knowledge base shows that AutoRAG's winning config (chunk 150, overlap 30, top-k 3, dense) achieves the same recall as a naive baseline while cutting context size by 88%, from 1,367 to 165 words. The approach scales from a local Ollama setup to Red Hat OpenShift AI in production with no code changes, simply swapping the backend. Importantly, AutoRAG improves retrieval quality but does not make a 1B-parameter model fully reliable on its own. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [RAG Explained Simply with a Real Project](https://daily.dev/posts/rag-explained-simply-with-a-real-project-pgy15jzg7) · freeCodeCamp · 23 upvotes · 1 comments - [Bringing custom knowledge to agents with AutoRAG](https://daily.dev/posts/bringing-custom-knowledge-to-agents-with-autorag-ckfjzih95) · Red Hat Developer · 0 upvotes · 0 comments - [RAG vs. long-context LLMs: A side-by-side comparison](https://daily.dev/posts/rag-vs-long-context-llms-a-side-by-side-comparison-96pemf4d7) · Meilisearch · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AutoRAG: Optimizing RAG for small models","url":"https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd"},"datePublished":"2026-08-04T07:12:36.501Z","dateModified":"2026-08-04T07:12:59.357Z","description":"RAG pipelines have many tunable parameters—chunk size, overlap, top-k, retrieval method—and picking them by intuition leads to poor results, especially with...","image":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","thumbnailUrl":"https://media.daily.dev/image/upload/s--P4t4XyoV--/f_auto/v1722860399/public/Placeholder%2001","isAccessibleForFree":true,"articleSection":"Red Hat Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Red Hat Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/2a8895f3216c4900bdc455585a4ad3e4","url":"https://daily.dev/sources/rhdev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/autorag-optimizing-rag-for-small-models-xdmzvdgcd","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,rag,vector-search","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Red Hat Developer","item":"https://daily.dev/sources/rhdev"},{"@type":"ListItem","position":3,"name":"AutoRAG: Optimizing RAG for small models"}]} ```