#debugging

Reveal the hidden 'thinking' process of an LLM. UI shows: 1. Original User Query. 2. Internal Thinking steps (hidden from usual output). 3. Final Conclusion. Highlight keywords that triggered transitions between thoughts. Use a clean, educational layout to help debug prompt logic.

Profile and optimize performance. Tools: 1. Chrome DevTools (Performance, Lighthouse). 2. React DevTools Profiler. 3. Node.js --prof and clinic.js. 4. Bundle analysis (webpack-bundle-analyzer). 5. Database query analysis (EXPLAIN). 6. APM tools (New Relic, DataDog). Focus on: render performance, bundle size, API latency, memory usage. Measure before optimizing. Profile in production-like environments.

I have a bug in my Python code. The function is supposed to calculate the factorial of a number, but it's returning an incorrect result for inputs greater than 5. Here is the code: [paste code here]. Help me find and fix the bug.

I'm getting the following error message in my browser console: "Uncaught TypeError: Cannot read properties of undefined (reading 'map')". Explain what this error means in simple terms, what are the common causes for it, and how I can go about debugging it in my JavaScript code.

Debug efficiently with systematic approach. Process: 1. Reproduce the bug consistently. 2. Isolate the problem (binary search through code). 3. Form hypothesis about cause. 4. Test hypothesis (add logging, use debugger). 5. Fix the root cause, not symptoms. 6. Verify fix doesn't break other functionality. 7. Add test to prevent regression. Techniques: rubber duck debugging, print statements, breakpoints, stack traces. Read error messages carefully. Check recent changes (git blame). Search Stack Overflow. Take breaks if stuck. Document solution. Prevention: write tests first, code reviews, static analysis.

Handle errors and log effectively. Practices: 1. Catch errors at boundaries. 2. Specific error types vs generic. 3. User-friendly error messages. 4. Detailed logs for debugging. 5. Structured logging (JSON). 6. Log levels (ERROR, WARN, INFO, DEBUG). 7. Correlation IDs for tracing. 8. Never log sensitive data. Use Winston, Pino, or similar. Centralize logs with ELK or Datadog. Monitor error rates.

Debug LLM applications with LangSmith. Features: 1. Trace every LLM call. 2. View chain execution steps. 3. Latency and token analysis. 4. Error tracking and debugging. 5. Dataset creation from logs. 6. Evaluation and testing. 7. Feedback collection. 8. Cost monitoring. Essential for production LLM apps. Use to identify bottlenecks and optimize prompts.

Detect and prevent memory leaks. Techniques: 1. Use browser DevTools memory profiler. 2. Heap snapshots comparison. 3. Clear event listeners on cleanup. 4. Unsubscribe from observables. 5. Clear timers and intervals. 6. Weak references for caches. 7. Avoid global variables accumulation. 8. Monitor production with tools like Sentry. Common causes: closures, forgotten subscriptions, detached DOM. Implement cleanup in useEffect/componentWillUnmount.