RAG (Retrieval-Augmented Generation) is the bridge between knowledge and LLMs. It solves a critical problem: how do you make LLMs aware of private data without expensive fine-tuning?
The RAG Problem
LLMs have a knowledge cutoff. They don't know about your company's proprietary data, latest documents, or internal policies. You have three options:
- Fine-tuning: Expensive, slow, requires retraining
- Prompt Injection: Shove context into the prompt (loses relevance with large data)
- RAG: Smart retrieval + generation (fast, flexible, cost-effective)
How RAG Works
RAG has three phases:
- 1. Indexing: Convert documents into embeddings and store in vector DB
- 2. Retrieval: Find relevant documents for the user's query
- 3. Generation: Pass retrieved context + query to LLM for answer
Vector Embeddings: The Foundation
Embeddings convert text into numbers that LLMs understand:
- Sentence Embeddings: "The cat sat on the mat" → [0.1, -0.3, 0.8, ...]
- Semantic Similarity: Similar meanings have similar embeddings
- Dimension Matters: OpenAI's text-embedding-3 uses 3072 dimensions
Vector Databases: The Engine
Popular choices:
- Pinecone: Fully managed, serverless, easy to start
- Weaviate: Open-source, flexible, good for enterprises
- Milvus: Open-source, high-performance, self-hosted
- Chroma: Simple, in-memory, great for prototyping
Advanced RAG Patterns
Beyond basic retrieval:
- Multi-stage Retrieval: Coarse-to-fine search for accuracy
- Reranking: LLM reranks retrieved docs for relevance
- Query Expansion: Generate multiple query variations
- Adaptive Retrieval: Decide when to retrieve based on query type
Common Pitfalls
What goes wrong:
- ❌ Chunking too aggressively (loses context)
- ❌ Not deduplicating documents
- ❌ Ignoring embedding quality
- ❌ Retrieving irrelevant documents (garbage in = garbage out)
- ❌ No monitoring of retrieval quality
Evaluation Metrics
How to know if your RAG is working:
- Retrieval Accuracy: Did we retrieve the right documents?
- Generation Quality: Are LLM answers correct and helpful?
- Latency: Is it fast enough?
- Cost: How many tokens are we using?
Quick Win: Start with Chroma + OpenAI embeddings. It's free to prototype and has great Python support.