Rag Retrieval Augmented Generation

PART 15 — RAG (Retrieval-Augmented Generation)

RAG bridges the gap between frozen model weights and real-time private databases.

SDE Takeaway: RAG is just an Information Retrieval (IR) pipeline glued to a prompt. 1. Ingestion: Map/Reduce jobs parse PDFs, chunk text, call embedding APIs, and write to a Vector DB. 2. Query: Microservice takes user string, embeddings it, queries the DB, concatenates the top-k results into a string, and injects it into the LLM payload.

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