source-summary
Zero-hallucination RAG design for 10M documents
Zero-hallucination RAG design for 10M documents
A viral system design problem reportedly asked in Google L5 interviews challenges candidates to build a RAG pipeline for 10 million documents that guarantees zero hallucination. The outlined architecture relies on hybrid BM25 and embedding retrieval, ANN search with a reranking stage, strict constrained generation, source confidence scoring, and citation-backed answers. At this scale, the approach argues that retrieval quality outweighs frontier model capability, necessitating continuous adversarial evaluation, caching, and deep observability.
Key Takeaways
- Use hybrid retrieval (BM25 + embeddings) to balance exact keyword matching with semantic understanding at massive scale
- Implement constrained generation tied strictly to retrieved context, enforced by citation-backed responses and a low-confidence fallback to 'insufficient evidence'
- Layer ANN retrieval with a dedicated reranker to improve precision across millions of documents
- Score every chunk on freshness, trust, overlap, and consistency to prevent low-confidence context from influencing generation
- Prioritize retrieval architecture, continuous evaluation, and observability over raw frontier model power for 10M-document workloads
Related Concepts
- retrieval-augmented-generation
- hybrid-search
- approximate-nearest-neighbor
- constrained-generation
- hallucination-mitigation
Related Entities
Related: overview.