This is basically a full system design in one breath.
Start by clarifying requirements and constraints, then walk through the architecture end-to-end, making explicit trade-offs at each stage to meet p99 latency and cost goals. Emphasize how each component (ingestion, retrieval, re-ranking, caching, orchestration, guardrails) contributes to the overall performance and cost profile, and justify your choices with data-driven reasoning.
Pro tip: Quantify the impact of each design decision on latency and cost—for example, estimate the p99 latency budget per component and show how caching and re-ranking trade-offs keep you under 1.5s. Also, mention that you'd validate with load testing and continuous monitoring to ensure SLAs are met in production.
Ask about expected QPS, document volume, update frequency, and cost budget. Confirm that p99 latency <1.5s is end-to-end and identify any compliance or safety requirements.
Outline document ingestion (batch/streaming), chunking strategy (e.g., semantic or fixed-size with overlap), embedding model choice (e.g., text-embedding-3-small for cost/latency), and index type (e.g., HNSW for low-latency ANN search).
Describe hybrid retrieval (dense + sparse) and re-ranking with a lightweight cross-encoder or LLM-based re-ranker, balancing accuracy and latency. Discuss caching strategies (e.g., query cache, embedding cache) to reduce redundant computation.
Explain prompt construction with retrieved context, LLM selection (e.g., GPT-4o-mini for cost/latency), and safety guardrails (e.g., input/output moderation, PII redaction, fallback responses).
Propose load testing to measure p99 latency, cost tracking, and monitoring for drift. Discuss iterative improvements based on metrics.
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The deletion problem is sneakier than it sounds.
Start by clarifying the requirements: scale, latency, consistency, and tenant isolation guarantees. Then propose a design that handles updates/deletions via soft deletes with periodic compaction, and enforces tenant isolation through namespace partitioning and metadata filtering. Finally, discuss trade-offs and alternatives.
Pro tip: Emphasize the importance of measuring recall and latency after updates, and consider using a write-ahead log for durability. Also, mention that tenant isolation should be enforced at multiple layers (e.g., index, query, and access control) to prevent data leakage.
Ask about scale (number of documents, tenants), update frequency, latency requirements, and consistency needs. This shapes the design choices.
Propose using soft deletes (tombstones) and versioning, with periodic compaction to reclaim space and maintain performance. Discuss how to handle updates efficiently without full reindexing.
Suggest partitioning the index by tenant (e.g., separate namespaces or collections) and using metadata filtering to ensure queries only access the tenant's data. Mention access control at the API layer.
Compare approaches: separate indices per tenant vs. shared index with filters. Discuss trade-offs in terms of cost, performance, and isolation guarantees.
Recap the proposed solution, highlighting how it meets the requirements, and suggest metrics to monitor (e.g., recall, latency, isolation breaches).
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I actually liked this part of the question.
Start by framing failure modes as expected events in a production ML system, then systematically address each one with detection, fallback, and recovery strategies. Emphasize graceful degradation, observability, and iterative improvement based on real-world data.
Pro tip: Show that you think about failure modes proactively by designing systems that assume failures will happen, and highlight how you'd use metrics and logs to continuously refine your handling strategies.
List the failure modes mentioned and briefly explain why each is critical in an ML system, showing awareness of their impact on user experience and system reliability.
Describe how you would detect each failure mode in real-time, using metrics, logging, and alerting to ensure visibility and quick response.
For each failure mode, outline specific fallback mechanisms (e.g., default responses, cached results, retries with backoff) and recovery procedures to maintain service continuity.
Explain how the system should degrade gracefully, possibly with reduced functionality, while communicating status to users or downstream services.
Emphasize the importance of post-mortems, A/B testing, and continuous monitoring to refine failure handling over time.
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Start by defining offline metrics that measure retrieval quality (e.g., recall@k, nDCG) and online metrics that capture user engagement and business impact (e.g., CTR, dwell time, conversion). Then design an A/B test with careful randomization, sufficient power, and guardrail metrics to compare the two retrieval approaches. Emphasize the trade-offs between offline and online metrics and how they inform iteration.
Pro tip: Highlight the importance of aligning offline metrics with online outcomes and using a pre-registered analysis plan to avoid p-hacking. Mention that at OpenAI, you'd also consider latency and cost as key constraints in production.
Select metrics that evaluate retrieval quality on a labeled dataset, such as recall@k, precision@k, mean reciprocal rank (MRR), and normalized discounted cumulative gain (nDCG). These should reflect the relevance of retrieved documents to the query.
Choose metrics that measure user behavior and business impact in production, such as click-through rate (CTR), dwell time, conversion rate, and query success rate. Also include system metrics like latency and cost per query.
Set up an A/B test with random assignment of users or sessions to control (BM25 + cross-encoder) and treatment (dense-only). Ensure sufficient sample size via power analysis, and define the primary metric and guardrail metrics.
Compare the two approaches on both offline and online metrics. Use statistical tests to determine significance, and check for novelty effects or segment-level differences. Consider trade-offs between relevance, latency, and cost.
Based on results, decide whether to adopt the new approach, iterate further, or combine methods. Document learnings and consider long-term impact through holdback experiments.
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10M docs at roughly 512-dimensional float32 embeddings is about 20GB for the raw vectors before any overhead.
Start by clarifying assumptions (document size, query type, latency SLA) and then break the problem into three parts: index size, throughput, and cost. Use simple arithmetic with round numbers to estimate storage, compute, and monthly expenses, and state your reasoning clearly.
Pro tip: Always state your assumptions explicitly and sanity-check the final numbers against known benchmarks (e.g., a single server can handle ~1000 QPS for simple queries). This shows you think like an engineer, not just a calculator.
Ask about document size, query complexity, latency SLA, and whether the system is read-heavy. Assume average document size (e.g., 10 KB) and simple keyword queries for estimation.
Calculate raw data size (10M docs * 10 KB = 100 GB) and multiply by an index overhead factor (e.g., 2-3x for inverted index). Result: ~200-300 GB of index storage.
Assume each server can handle 500-1000 QPS for simple queries. For 50 QPS, one server suffices, but for redundancy and peak load, use 2-3 servers. Consider sharding if index is large.
Use cloud pricing: storage ~$0.10/GB-month, compute ~$0.10/hour for a mid-tier instance. For 300 GB storage: $30/month; for 3 instances: ~$216/month. Total ~$250/month, plus data transfer and management overhead.
Verify numbers are reasonable (e.g., 50 QPS is low, so cost is dominated by storage). Summarize key figures and mention potential optimizations like compression or caching.
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Honestly a meta-question about prioritization under pressure.
Start by framing the 15-minute constraint as a forcing function for clarity, then present a ruthlessly prioritized version of the plan that focuses on the highest-impact, highest-uncertainty items. Explicitly state what you're cutting and why, showing that you understand trade-offs and can communicate them to stakeholders.
Pro tip: Tie every prioritization decision back to measurable impact on the end goal—at OpenAI, that often means model performance, safety, or scalability—and be ready to defend your cuts with data or clear reasoning.
Briefly restate the plan's objective and the 15-minute constraint to ensure alignment. Emphasize that the goal is a coherent, actionable version, not a comprehensive one.
Determine the fewest steps that must be taken to achieve a minimally viable outcome. Focus on items that are on the critical path and have the highest impact or risk.
Rank remaining items by expected impact and level of uncertainty. Include only those that are essential to validate the core hypothesis or deliver immediate value.
List the items you're omitting and give a concise rationale for each cut, such as lower impact, deferrable, or dependent on unresolved questions.
Conclude with a crisp summary of the prioritized plan and the cuts, then invite questions or suggestions to adapt further if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.