RAG Systems: Building AI That Uses Your Data
How to build retrieval-augmented generation apps that actually work. Embeddings, vector stores, and practical deployment.
⚡ What You'll Learn
- →Core concepts explained with real-world context
- →Practical implementation patterns
- →Common mistakes and how to avoid them
# RAG Systems
Retrieval-Augmented Generation (RAG) is the bridge between LLMs and your private data.
The Challenge
It's easy to build a demo RAG app. It's hard to build one that doesn't hallucinate or retrieve irrelevant context.
Embeddings Matter
Your choice of embedding model determines what "similarity" means. Generic models are okay, but domain-specific embeddings often yield much better retrieval results.
Chunking Strategies
Don't just split by character count. Split by semantic meaning. A paragraph is a unit of thought. A sentence is a unit of thought. A random 500-token chunk is noise.
Re-ranking
Retrieving the top-k documents is just the first step. Use a re-ranker model to score those candidates before feeding them to the LLM. This significantly improves response quality.