Best practices for optimizing retrieval-augmented generation systems

Retrieval-Augmented Generation (RAG) systems combine language models with real-time data retrieval for accurate, context-aware responses. Configuring these systems requires balancing retrieval accuracy, context enrichment, and generation quality.

This white paper offers strategies to optimize RAG pipelines and tackle issues when context relevance falls short. Key approaches include:

· Enriching context via text blocks, metadata, and data augmentation
· Filtering and multi-step queries to prioritize reliable sources
· Using metrics to evaluate answer relevance, context relevance, and groundedness

Explore options and examples to build effective RAG systems in this guide.