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RAG Fundamentals

📚 Agentic RAG⏱ 8 min⭐ 70 BASE XP

Why RAG?

Models are frozen in time when they finish training. Retrieval-Augmented Generation (RAG) gives them a search engine for your private data.

The standard RAG pipeline:

  1. Embed: Convert text documents into numerical vectors using models like text-embedding-3-large.
  2. Store: Save these vectors in a database designed for distance search (Pinecone, Qdrant).
  3. Retrieve: When a user asks a question, embed the question and find the "nearest" documents.
  4. Generate: Feed the retrieved documents to the LLM and ask it to answer based only on the context.
SYNAPSE VERIFICATION
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What is the primary purpose of Retrieval-Augmented Generation (RAG)?
To train standard models faster
To reduce API costs
To ground LLM responses in factual, external data to reduce hallucination
To compress context windows
Watch: 139x Rust Speedup
RAG Fundamentals | Agentic RAG — AI Agents Academy