--- title: AI Crash Course: Retrieval Augmented Generation (RAG) description: An explainer on Retrieval Augmented Generation (RAG) covers why general-purpose LLMs can't answer questions about proprietary or recent information due to... canonical: https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI Crash Course: Retrieval Augmented Generation (RAG) | daily.dev og:description: An explainer on Retrieval Augmented Generation (RAG) covers why general-purpose LLMs can't answer questions about proprietary or recent information due to... og:url: https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k og:image: https://api.daily.dev/og/posts/f2sOm1u5K.png og:image:alt: AI Crash Course: Retrieval Augmented Generation (RAG) 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. # AI Crash Course: Retrieval Augmented Generation (RAG) **[Telerik](https://daily.dev/sources/telerik)** · [@kgn](https://daily.dev/kgn) · 7 min read · 1 upvotes · 0 comments ## Summary An explainer on Retrieval Augmented Generation (RAG) covers why general-purpose LLMs can't answer questions about proprietary or recent information due to training data cutoffs, and how RAG solves this without fine-tuning. It walks through chunking documents into logical pieces, generating embeddings for those chunks and storing them in a vector database, and using semantic search to match a user's question to relevant chunks by meaning rather than keyword overlap. It notes RAG is often combined with keyword search, relational or graph databases, and APIs, and frames RAG as a set of architectural decisions rather than a single technology. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is retrieval augmented generation (RAG) and why do I need it instead of fine-tuning a model? RAG lets a language model retrieve relevant information from an external source at response time instead of having that information baked into its training. It avoids the time and cost of fine-tuning, works well when source data changes frequently (like updated documentation) or is private to a specific customer, and reduces the chance of the model hallucinating an answer it doesn't actually know. _daily.dev surfaces practical breakdowns like this for developers deciding how to ground their AI features._ ### Why does semantic search with embeddings work better than keyword search for RAG systems? Embeddings represent text as numerical vectors capturing meaning, so a RAG system can match a question to relevant document chunks even when they share no common words. For example, a question phrased as 'how much time do I get off after having a baby' can match a section titled 'Parental Leave' that never uses those words, something a keyword search for 'have' and 'baby' would miss. _developers building retrieval pipelines can track semantic search techniques and gotchas on daily.dev._ ### Why do RAG systems break documents into chunks before indexing them? Chunking splits documents into smaller logical units, such as a paragraph or section, before generating embeddings and indexing them for retrieval. This limits how much text has to fit into a model's context window and makes it easier for the retrieval system to pinpoint the specific piece of relevant information rather than searching an entire large document. _daily.dev helps developers designing RAG pipelines keep up with chunking and retrieval best practices._ --- Tags: [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search), [#embeddings](https://daily.dev/tags/embeddings) [View this post on daily.dev](https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k) ```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":"AI Crash Course: Retrieval Augmented Generation (RAG)","url":"https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k"},"datePublished":"2026-09-02T21:46:01.023Z","dateModified":"2026-09-03T18:00:15.108Z","description":"An explainer on Retrieval Augmented Generation (RAG) covers why general-purpose LLMs can't answer questions about proprietary or recent information due to...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/bb767c07401b21aaf9c303a338985a1c?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/bb767c07401b21aaf9c303a338985a1c?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Telerik","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":"Person","name":"Kathryn Grayson Nanz","url":"https://daily.dev/kgn","image":"https://lh3.googleusercontent.com/a-/AOh14GjMQ94nxRC-E6Fy6RQoZ1mS78I89PCDzQajcy7GCA=s100","interactionStatistic":{"@type":"InteractionCounter","interactionType":{"@type":"EndorseAction"},"userInteractionCount":150}},"commentCount":0,"discussionUrl":"https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,rag,vector-search,embeddings","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Telerik","item":"https://daily.dev/sources/telerik"},{"@type":"ListItem","position":3,"name":"AI Crash Course: Retrieval Augmented Generation (RAG)"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/ai-crash-course-retrieval-augmented-generation-rag--f2som1u5k#faq","mainEntity":[{"@type":"Question","name":"What is retrieval augmented generation (RAG) and why do I need it instead of fine-tuning a model?","acceptedAnswer":{"@type":"Answer","text":"RAG lets a language model retrieve relevant information from an external source at response time instead of having that information baked into its training. 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