The tricky part isn't the LLM call itself, it's the schema variability.
Start by clarifying requirements and constraints, then propose a modular pipeline that classifies log types and routes them to schema-specific extraction prompts or models. Emphasize validation, fallback mechanisms, and scalability while discussing trade-offs between accuracy, cost, and latency.
Pro tip: Mention that you would use a two-stage approach: first classify the log type with a lightweight model, then use a specialized extraction prompt for that schema, and always include a validation layer to catch hallucinations and fall back to regex or human review.
Ask about log volume, latency requirements, schema variability, and accuracy expectations. Determine if real-time or batch processing is needed and what downstream systems consume the extracted fields.
Propose a pipeline with stages: ingestion, log type classification, schema-specific extraction, validation, and output. Use a router to direct logs to the appropriate extraction component based on classified type.
For each log type, design a prompt that includes the schema definition and few-shot examples. Consider using function calling or JSON mode to enforce structured output. Discuss fine-tuning vs. prompt engineering trade-offs.
Add a validation layer to check extracted fields against expected types, ranges, and patterns. If validation fails, fall back to rule-based parsing or flag for human review to maintain reliability.
Discuss batching, caching, and using smaller models for classification. Consider asynchronous processing and monitoring for drift. Mention cost optimization by routing only complex logs to LLMs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.