RAG: The Complete Guide to Retrieval-Augmented Generation 2026
RAG combines LLMs with external knowledge retrieval to produce more accurate, up-to-date, and grounded responses.
How RAG Works
- Indexing: Documents chunked and embedded into a vector database
- Retrieval: User query embedded and matched against stored vectors
- Augmentation: Retrieved context added to the LLM prompt
- Generation: LLM produces response grounded in retrieved context
RAG vs Fine-Tuning
| Factor | RAG | Fine-Tuning |
|---|---|---|
| Knowledge updates | Instant | Requires retraining |
| Hallucination risk | Lower | Higher |
| Cost | Lower (per-query) | Higher (upfront) |
| Implementation | Moderate | Complex |
Vector Databases
| Database | Type | Best For |
|---|---|---|
| Pinecone | Managed | Production, ease of use |
| Weaviate | Open-source | Hybrid search |
| Qdrant | Open-source | Performance |
| Chroma | Open-source | Development, prototyping |
| pgvector | Extension | Existing PostgreSQL users |
Best Practices
- Chunk size: 200-500 tokens with overlap
- Hybrid search: combine vector + keyword
- Rerank results with cross-encoder
- Evaluate with RAGAS framework
- Use metadata filtering for better precision
FAQ
Q: When should I use RAG vs fine-tuning?
A: Use RAG when knowledge changes frequently or you need source attribution. Use fine-tuning for domain-specific language or style adaptation.
Q: How much does RAG cost?
A: Vector DB: $0-70/month. Embedding API: $0.02-0.10/1K docs. LLM inference: varies by model. Total: typically $50-200/month for moderate use.