Find documents by meaning across 50 M passages: BM25 and a fine-tuned bi-encoder fused by RRF, a cross-encoder re-ranker, hard negatives, synthetic queries and index migrations: one-hour boards at three levels, with the theory.
Machine-learning engineers designing data, training and model systems.
Your approach: Explain the data, learning or model lifecycle in the brief. Support relevant quality and operating targets with evidence.
Design Semantic Document Search. Find documents by meaning across 50 M passages: BM25 and a fine-tuned bi-encoder fused by RRF, a cross-encoder re-ranker, hard negatives, synthetic queries and index migrations: one-hour boards at three levels, with the theory. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for Semantic Document Search. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: Permissions? Languages?
Identify users, required behavior and exclusions. Answer: What is searched? How do people search?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: What comes back? Success?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for Semantic Document Search. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: Labels? Latency, freshness? Permissions? Languages?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.