Phase 4: Retrieval Quality & Grounded Answers
7.4 relevance
Score Breakdown
technical depth 8
novelty 7
actionability 8
community 5
strategic 6
personal 9
Scored daily by a customisable AI persona to surface the most relevant engineering leadership news.
Retrieval quality and grounded answers for AI, directly relevant to AI/ML and data engineering.
Summary
RAG systems hallucinate because Top-K vector retrieval returns the closest chunks (e.g., a holiday calendar scoring 0.81) rather than relevant ones. Phase 4 fixes this with two-stage retrieval: wide HNSW search (Top-20) followed by a reranker model that scores query-chunk pairs for true relevance. It also adds relevance thresholds for abstention ("I don't know"), grounded prompts with citations, and hybrid search to catch what vector search misses.