Practiced one out loud? Paste your answer into the free self-check and get a score in 30 seconds
What the interviewer is testing
Whether you rely on enforcement and validation rather than prompt wording alone, and whether you design schemas that the model can fill correctly.
Answer framework
- Enforce the format: use the provider's structured output or JSON-schema mode, or constrained decoding (grammar-guided generation) when self-hosting.
- Validate every response against the schema in code, for example with Pydantic or a JSON Schema validator.
- Repair on failure: retry once or twice with the validation error included in the prompt, then fall back or escalate.
- Design the schema: keep it flat, use enums for categories, make optional fields explicit and allow null instead of forcing guesses.
- Monitor the parse failure rate and the rate of empty or default values in production.
Sample answer
I treat structured output as an engineering problem, not a prompting problem. First I enforce the format at generation time: hosted APIs offer a JSON-schema or structured output mode, and with a self-hosted model I use constrained decoding so only tokens that keep the output valid can be sampled. That removes most syntax errors.
Valid JSON can still be wrong, so I validate the parsed object against a Pydantic model: types, enums, ranges and required fields. If validation fails, I send one repair attempt that includes the original output and the exact validation error, and if that also fails I return a clear error or route to a fallback instead of passing bad data downstream.
Schema design matters as much as enforcement. For an invoice extractor I would use a flat object, an enum for currency, explicit nullable fields for values that may be missing, and a short description per field. If a model is forced to fill a required field it cannot find, it will invent one, so allowing null is safer. I then track parse failures and null rates per field as quality signals.
Likely follow-ups
Common mistakes
- Relying on a prompt instruction alone for production parsing.
- Skipping semantic validation because the JSON parsed.
- Making every field required, which pushes the model to invent values.
- Retrying without telling the model what was wrong.
- Using a high temperature for extraction tasks.
The 0 to 1 AI Engineer Interview Playbook covers the answer frameworks, system-design walkthroughs and an 8-week plan.
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