Step-by-Step: Build a RAG System in Python (Reduce LLM Hallucinations)

LLMs hallucinate. That’s not a bug. It’s how they work.

If you’re building anything production-facing, relying on raw LLM output is a bad decision.

RAG (Retrieval-Augmented Generation) fixes this by grounding responses in real data.

This guide walks through a working implementation:

What you’ll build:

Document → Embedding pipeline
Vector search using FAISS
Retrieval function
LLM-based answer generation

Stack used:

sentence-transformers
FAISS
OpenAI API

Key concepts covered:

Why embeddings matter
How retrieval improves accuracy
How to structure prompts for grounded responses

Also includes:

Full working code
Common mistakes (chunking, overlap, retrieval issues)
Beginner → production improvements

If you’re building AI apps, this is foundational.

Full guide with code:
👉 How to Build a RAG System (Step-by-Step Guide)