RAG Explained: Give an LLM Your Own Knowledge

An LLM doesn't know your company's docs, last week's news, or your private data — and it'll confidently make things up. RAG fixes that: retrieve the relevant facts, put them in the prompt, then generate. It's the backbone of almost every real LLM app. Here's the full pipeline, live.

📚 See it answer from a private knowledge base: https://dev48v.infy.uk/ai/days/day15-rag.html

Retrieve → Augment → Generate

1. Embed the question into a vector (last two posts: embeddings + vector DBs).
2. Retrieve the top-k most similar chunks from your knowledge base.
3. Augment the prompt: system instructions + the retrieved chunks as context + the question.
4. Generate — the model answers grounded in those chunks, and can cite them.

In the demo, flip RAG off and the bare model hallucinates or refuses — it never saw your data. Flip it on and the answer is correct and sourced.

Two pipelines

• Offline (indexing): chunk your docs → embed → store in a vector DB.
• Online (querying): embed question → search → build prompt → generate.

RAG vs fine-tuning

RAG adds knowledge (fresh, private, citable) without retraining. Fine-tuning changes behavior/style. Most teams reach for RAG first.

Where it breaks

Bad retrieval → bad answer. That's why reranking, HyDE, and Corrective-RAG (earlier posts) exist — they all make the retrieve step better.

🔨 Build it (chunk → embed → upsert → retrieve top-k → augment → generate + cite) on the page: https://dev48v.infy.uk/ai/days/day15-rag.html

Part of AIFromZero. 🌐 https://dev48v.infy.uk