RAG Development

Comprehensive knowledge base for building production-grade Retrieval-Augmented Generation systems.

When to Use

• Building a new RAG pipeline from scratch
• Choosing chunking strategy, embedding model, or vector database
• Implementing hybrid search, re-ranking, or contextual retrieval
• Evaluating RAG quality with RAGAS or DeepEval
• Optimizing production RAG for cost, latency, or accuracy
• Designing multi-tenant RAG with access control
• Upgrading from naive RAG to advanced patterns

Quick Start Recommendation

For 80% of use cases, start with:

1. Chunking: Recursive character splitting at 512 tokens, 10-15% overlap
2. Embedding: OpenAI text-embedding-3-small (best value) or Cohere embed-v4 (best accuracy)