The 60-second answer
Define the task, data boundary, and failure modes before choosing the model/prompt/retrieval/tool architecture. Explain the core model behavior and the training/inference distinction with concrete tensor or system details where relevant.
Build the answer in this order
1
Define the mechanism
Define the task, data boundary, and failure modes before choosing the model/prompt/retrieval/tool architecture.
2
Explain the architecture
Explain the core model behavior and the training/inference distinction with concrete tensor or system details where relevant.
3
Compare trade-offs
Evaluate task quality, factuality, safety, latency, and cost on representative slices.
4
Close with serving + evaluation
Design observability, fallbacks, versioning, and rollback for production changes.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
01Requirements
02Data
03Model / Retrieval
04Serving
05Monitor
Senior-level signal
- Separate model errors from retrieval/tool/orchestration errors so debugging and ownership are clear.
- Include adversarial and distribution-shift testing, not only benchmark averages.
What the interviewer is really testing
Understanding beyond prompting: architecture, retrieval, evaluation, inference, safety, latency, cost, and failure recovery.
Likely follow-up questions
What changes at 10× traffic or data volume?
Which failure mode would you monitor first in production?
How would you evaluate this offline and online before rollout?
Common weak-answer patterns
- Jumping to a model before defining the product contract.
- Listing components without bottlenecks, metrics, or failure handling.
- Ignoring data quality, serving latency, monitoring, and iteration.