Design a Logging Strategy for Any Application
Design structured logging with appropriate levels, context fields, and observability integrations.
The Prompt
Design a structured logging strategy for the following application. Provide: 1. Log levels (debug/info/warn/error) with usage guidelines — what belongs at each level 2. Required context fields for every log entry (request ID, user ID, service name, etc.) 3. Logger setup with your recommended library 4. Example log calls for common scenarios 5. What NOT to log (secrets, PII, large payloads) 6. Sampling strategy for high-volume debug logs 7. Integration with your observability stack Application type: [NEXTJS API / EXPRESS / FASTAPI / etc.] Observability stack: [DATADOG / GRAFANA / AWS CLOUDWATCH / etc.] Expected log volume: [e.g., ~50,000 req/day]
Example Output
Recommended pino for Node.js with a custom serializer that redacts Authorization headers and password fields. Defined 5 context fields (requestId, userId, route, duration, statusCode), set debug sampling at 10% in production, and provided Datadog log forwarding config.
FAQ
Which AI model is best for Design a Logging Strategy for Any Application?
Claude Sonnet 4 — excellent at system design and operational best practices.
How do I use the Design a Logging Strategy for Any Application prompt?
Copy the prompt, replace the [BRACKETED] placeholders with your specific information, and paste into your preferred AI assistant (ChatGPT, Claude, Gemini, etc.). Recommended pino for Node.js with a custom serializer that redacts Authorization headers and password fields. Defined 5 context fields (requestId, userId, route, duration, statusCode), set debug sampling at 10% in production, and provided Datadog log forwarding config.
Model Recommendation
Claude Sonnet 4 — excellent at system design and operational best practices.