Few-Shot Logging Best Practices

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Pattern-based structured logging implementation for ML systems

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Implement structured logging based on these patterns.

Example 1 - Request Lifecycle:
```python
logger.info("request_started", extra={
    "request_id": request_id,
    "endpoint": endpoint,
    "user_id": user_id,
    "timestamp": datetime.utcnow().isoformat()
})
```

Example 2 - LLM Call Instrumentation:
```python
logger.info("llm_call_complete", extra={
    "model": model_name,
    "input_tokens": input_count,
    "output_tokens": output_count,
    "latency_ms": latency,
    "cache_hit": was_cached
})
```

Example 3 - Error Context:
```python
logger.error("retrieval_failed", extra={
    "error_type": type(e).__name__,
    "query_id": query_id,
    "attempted_sources": sources,
    "fallback_used": fallback_result is not None
}, exc_info=True)
```

Now implement logging for:
Component: {{component_name}}
Operations: {{operations_list}}
Debug Requirements: {{debug_needs}}

Details

Category

Coding

Use Cases

Logging implementationObservability setupDebug infrastructure

Works Best With

claude-sonnet-4-20250514gpt-4o
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