@adxtyahq ·
“design a RAG pipeline for 10M docs with zero hallucination” apparently this was asked in a Google L5 interview round. came across it somewhere on the internet and honestly it’s a way more interesting system design problem than most classic distributed systems questions 1. ingest + normalize docs - remove duplicates, standardize formats, extract metadata, maintain version history 2. hybrid retrieval (BM25 + embeddings) - BM25 handles exact keyword matching while embeddings capture semantic meaning - semantic search alone usually struggles with precision at massive scale 3. ANN retrieval + reranking - ANN (Approximate nearest neighbor ) quickly pulls top candidate chunks from millions of docs - then a reranker rescoring step improves relevance by deeply comparing query vs retrieved chunks 4. source confidence scoring - every retrieved chunk gets scored based on freshness, trust level, overlap and retrieval consistency - low-confidence context should never heavily influence generation 5. constrained generation - the model is only allowed to answer using retrieved context (nothing new to be invented outside of the retrieved context) 6. citation-backed responses - every major claim links back to exact chunks, documents or timestamps 7. hallucination fallback layer - if retrieval confidence drops below a threshold: “insufficient evidence found” 8. continuous evals - run adversarial queries, retrieval recall benchmarks and hallucination tests continuously 9. caching + memory layer - cache high-frequency enterprise queries and retrieval paths (improves latency and output) 10. observability everywhere - trace retrieval paths, chunk rankings, token attribution and failure points Also at 10M docs, retrieval quality matters more than the frontier model itself.
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