Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems

Understand

Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses.

• Originating from the simple 'retrieve-then-read' approach, the RAG framework has evolved into a highly flexible and modular paradigm.
• A critical component, the Query Rewriter module, enhances knowledge retrieval by generating a search-friendly query.
• This method aligns input questions more closely with the knowledge base.