RAG Architecture

Summary

Retrieval-Augmented Generation — retrieving relevant context from a knowledge base and injecting it into a prompt so the model answers grounded in real data instead of memory alone.

• 1Build the full RAG loop end-to-end: embed, store, retrieve, inject into prompt, generate.
• 2Learn where each failure mode (bad retrieval, bad chunking, weak prompt) shows up in outputs.
• 3Compare RAG output quality against the same model with no retrieved context.