RAG — How to Make LLMs Tell the Truth About Your Data

LLMs hallucinate. That’s a fact. RAG (Retrieval Augmented Generation) is an architectural pattern that dramatically mitigates this problem — and opens the door for enterprise AI applications.

The Problem: LLMs Don’t Know Your Data¶

GPT-4 has encyclopedic knowledge. But it doesn’t know your internal processes, products, or clients. And when you ask about something it doesn’t know? It makes it up. Confidently.

How RAG Works¶

• Indexing: Your documents → chunking → embeddings → vector DB
• Retrieval: User query → embedding → similarity search → top-K documents
• Generation: Prompt = system instructions + retrieved context + user query → LLM → answer

Chunking — The Devil Is in the Details¶

Chunks that are too small lose context. Chunks that are too large waste the context window. Our sweet spot: 500–1,000 tokens with a 100-token overlap. For structured documents, chunk by section.

Retrieval Strategies¶

Hybrid search (vector + BM25) works better for technical queries. Re-ranking models (cross-encoders) further refine results.

Evaluation¶

We measure: Faithfulness (does it match the context?), Relevance (is the context relevant?), Answer correctness. We use the RAGAS framework.

RAG Is an Enterprise AI Must-Have¶

If you’re building an AI application over company data, RAG is the foundation. Quality depends on chunking strategy, retrieval pipeline, and prompt design.