RAG System Design: Empowering Large Models with Real-time Knowledge Capabilities
What is RAG
RAG (Retrieval-Augmented Generation) enables large models to access the latest and accurate external knowledge by retrieving relevant documents before generation, effectively addressing issues of hallucination and outdated knowledge.
Core Architecture
User Query → Query Rewriting → Retrieval → Re-ranking → Prompt Assembly → LLM Generation
Hybrid Retrieval
Combines vector retrieval and keyword retrieval, merging results using Reciprocal Rank Fusion.
Re-ranking
Uses Cross-Encoder to fine-rank retrieval results, significantly improving the relevance of the top N results.
Advanced Optimization
- HyDE: First let LLM generate a hypothetical answer, then use the answer for retrieval
- Multi-Query: Generate multiple retrieval queries, merge results
- Adaptive Retrieval: Decide whether to retrieve based on confidence
Evaluation Metrics
| Metric | Target Value |
|---|---|
| Recall | > 90% |
| Faithfulness | > 85% |
| Relevance | > 90% |
RAG is not simply "retrieval + generation", but a system that requires careful design. Hybrid retrieval, re-ranking, and query rewriting are key to improving effectiveness.
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