zero-hallucination-rag-design-for-10m-documents.txt

# Mobile Capture 20260522T102630Z-331582187

Captured: 2026-05-22T10:26:30.000Z
Source: telegram
From: @fatfat_pig

## Content

“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 frontier model itself