Franck Lebeau

You’re in a Machine Learning interview at Perplexity, and the interviewer asks: “Why do we need hybrid search? Isn’t vector search with embeddings enough?” Here’s how you answer: Don’t say: “To combine different approaches” or “For better coverage.” Too generic. The real answer is the semantic-lexical gap. Your embeddings understand meaning but ignore exact matches. Vector search alone misses the forest for the trees - or worse, the exact product code the user typed. Here’s why pure vector search fails: Your query is “iPhone 15 Pro Max 256GB.” Vector search returns “iPhone 15 Pro with lots of storage” and “latest flagship phone specs.” But the user wants EXACT model + EXACT capacity. Semantic understanding ≠ Precision matching. btw get this kinda content on your email for free, daily, subscribe to my newsletter -fullstackagents.substack.com… The retrieval failure modes are brutal: Pure vector search: > Query: “ML-2847 error code” → Returns: General ML troubleshooting (0% useful) > Query: “React 18.2.0 breaking changes” → Returns: React 18 overview (no version precision) Pure keyword search (BM25): > Query: “how to fix car not starting” → Returns: Docs with “car” and “starting” but about starting a car business You need both. Always. The performance gap across real benchmarks: - BM25 alone: 67% MRR@10 - Dense retrieval alone: 71% MRR@10 - Hybrid (proper fusion): 82% MRR@10 That’s 15% improvement over the “best” single method. In production, that’s thousands of better answers per day. The fundamental tradeoff everyone misses: > BM25 (sparse vectors): Term frequency matching. Perfect for exact keywords, acronyms, codes. Fails at synonyms. > Dense embeddings: Semantic similarity. Perfect for meaning, paraphrases. Fails at exact matches. This is why you can’t pick one. You need intelligent fusion. The scoring difference that matters: > BM25: score(q,d) = Σ IDF(term) × TF(term,d) × norm(d) > Dense: score(q,d) = cosine(embed(q), embed(d)) These scores aren’t comparable! BM25 gives 0-15, cosine gives 0.7-0.95. This is why naive averaging fails. You need score normalization. The fusion algorithms you must know: 1. Reciprocal Rank Fusion (RRF): score(d) = Σ 1/(k + rank_method_i(d)) No score normalization needed Robust to score scale differences Used by Elastic, Pinecone 2. Weighted combination: score(d) = α × norm(score_bm25) + (1-α) × norm(score_dense) Requires score normalization α typically 0.3-0.5 More control but more tuning “So how do you choose the hybrid ratio?” Interviewer leans in. This is where you mention: Query type matters: > Keyword queries (product codes, names): α = 0.7 (favor BM25) > Natural language questions: α = 0.3 (favor dense) > Hybrid queries (”best iPhone under $500”): α = 0.5 > Measure and tune on YOUR data. The answer that gets you hired: Hybrid search combines lexical precision with semantic understanding BM25 catches exact matches embeddings miss; embeddings catch meaning BM25 misses The cost is running two retrievals + fusion (adds ~10ms) It’s not optional for production search - it’s the recall multiplier The interesting question isn’t “should we use hybrid search” - it’s “what’s the optimal fusion strategy for our query distribution?” Use RRF? Simple but less control. Use weighted combo? More tuning but better fit. The answer: Start with RRF, measure the gap, upgrade if needed. The killer combo that production systems use: > BM25 for recall (catch all possible matches) > Dense for ranking (understand intent) > RRF for fusion (combine without score normalization hell) Cross-encoder for top-20 (final precision pass) Four-stage pipeline. Each stage does what it’s best at.