This one sprawled in a way I didn't expect.
Structure your answer as a layered reliability strategy: start with prompt design (clear instructions, system role, few-shot examples), then enforce constraints via API parameters (response_format, tool calling, temperature=0), add runtime validation and retries, and close with an evaluation loop using metrics like JSON validity rate and schema adherence. Emphasize trade-offs between strictness and flexibility, and how you'd iterate based on real-world failures.
Pro tip: Mention that even with strict JSON mode, you should always validate and handle parse errors gracefully—LLMs can still produce invalid JSON under edge cases, so a fallback or retry with a repair prompt is essential for production reliability.
Use a system message to define the assistant's role and strict output format, then a user message with the task and any few-shot examples of valid JSON. Explicitly instruct 'Return only JSON, no other text.'
Use response_format={'type': 'json_object'} if available, or tool/function calling with a JSON schema. Set temperature=0 and max_tokens appropriately to reduce variability and truncation.
Validate the response against a JSON schema (e.g., using Pydantic or jsonschema). If invalid, retry with a repair prompt or fallback logic. Log failures for analysis.
Measure success metrics like JSON validity rate, schema adherence, and latency. Test with diverse inputs, including edge cases, and iterate on prompt, parameters, or schema based on failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.