Embeddings & Retrieval
Find and rank candidates with lexical, semantic, and hybrid search.
Compare exact-match and semantic retrieval, measure ranking quality on fixtures, and combine both within a latency budget.
3 questions
Questions
Easy
1.5 h
Explain how embeddings and exact-term matching find candidates for a query, and why the top result is a ranking signal rather than a verified answer.
Medium
3 h
Rank four help articles by shared words and by cosine similarity, score both rankings against labels, and explain a dense miss and an accidental lexical success.
Hard
4 h
Design hybrid retrieval with reranking for a multi-tenant help center, choose a configuration from measured quality and latency gates, and keep access rules intact through timeouts and model migrations.
What you will be able to do
- Explain what an embedding is, and what it is not
- Compute cosine similarity for small vectors and check whether a higher or lower score ranks first
- Choose between lexical, dense, and hybrid search for a given type of query
- Explain how top-k, metadata filters, and access rules shape the returned set
- Explain why a retrieval score is a ranking signal, not confidence, relevance, or permission
- Implement lexical and cosine rankings with a deterministic tie rule
- Calculate precision@k, recall@k, and reciprocal rank from a ranked list
- Recognize a ranking that succeeds by accident rather than by evidence
- Identify the stage where a relevant item disappeared
Before you start
Suggested, not required.
No prerequisites: a good place to start.
Hands-on exercises
Interview rounds that use this topic
Topic boundaries
This topic finds and ranks candidates. RAG & Data Pipelines covers ingestion, grounding, citations, and the document lifecycle.
Related on CodeSnatch
System Design
Covers inverted indexes, analyzers, and BM25, then a hybrid BM25 plus vector query fused before a rerank.
System Design
Shows embedding retrieval and ANN serving as the first stage of a candidate, ranking, and re-ranking funnel.
Community article
A practitioner tour of embeddings, cosine similarity, ANN indexes, hybrid search, reranking, and tenant filters.
System Design
Builds BM25 candidate generation with a learned rerank on top, served across shards by scatter-gather.
Practice problem
Practice exact nearest-neighbor search: rank points by distance and keep the k closest with a heap.
AI Engineering
The next step after retrieval: how ranked passages become cited evidence for a generated answer.
AI Engineering
Carries embedding migration into a backfill that failed partway, with versioned indexes and a cutover gate.
System Design
Ranks prefix matches under a sub-100 ms budget, with n-gram matching for typos and a re-rank of extra candidates.