Linear Digressions It's RAG time: Retrieval-Augmented Generation
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Mar 2, 2026 A clear walk-through of Retrieval-Augmented Generation and why it powers document-aware chatbots. They cover how texts get chunked and embedded for fast similarity search. You hear the step-by-step retrieval and prompt composition process. They also dig into common failure modes like multi-hop reasoning and retrieval bottlenecks, plus when RAG is most useful and mitigation ideas.
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How RAG Works End To End
- Retrieval Augmented Generation (RAG) augments LLMs by retrieving relevant document chunks and inserting them into the prompt before generation.
- It embeds document chunks and the query into vectors, similarity-searches for nearby vectors, then supplies matching chunks as context to the LLM.
Chunk And Embed Documents For Fast Retrieval
- Chunk and embed large text corpora to make selective retrieval efficient instead of forcing the LLM to read everything.
- Store embeddings linked to original chunks so a similarity search can quickly return relevant text for a query.
RAG Struggles With Multi Hop Reasoning
- RAG often fails on multi-hop reasoning because required facts may be scattered across different chunks and not retrieved together.
- If retrieval misses one hop, the LLM lacks the chained context and the reasoning chain breaks down.