Why RAG matters for enterprise
Enterprise knowledge lives in documents, databases, APIs and tribal memory. RAG connects LLMs to this knowledge without retraining — making AI responses grounded, current and auditable.
The five-layer RAG architecture
Ingestion, chunking, embedding, retrieval and generation. Each layer has design choices that dramatically affect accuracy, latency and cost.
Advanced retrieval patterns
Hybrid search, re-ranking, query decomposition and contextual compression turn basic RAG into enterprise-grade knowledge retrieval.
Evaluation is non-negotiable
Production RAG needs faithfulness, relevance and hallucination metrics. Without eval, you are shipping guesses.