Retrieval-Augmented Generation (RAG) connects LLMs to proprietary data. However, simple vector queries often fetch irrelevant noise. We must optimize semantic chunking and response validation.
1. Advanced Semantic Chunking
Fixed-size chunking (e.g. 500 characters) cuts text in half, losing context. Use semantic boundary detection (like parsing headings or structural blocks) to preserve document relationships.
2. Hybrid Retrieval (Sparse + Dense)
Combine vector similarity search (dense) with keyword matching (sparse BM25) using reciprocal rank fusion (RRF) to retrieve both contextual topics and exact matching codes.
3. Structural Output Validation
Define rigid output structures using validation libraries like Zod on the server. If the LLM generates incorrect fields, trigger an automatic correction loop before returning the API response.