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Tools like LangSmith, Braintrust, and Helicone provide trace-level observability for LLM applications. They let you inspect every prompt, response, latency, and cost in production, making debugging and optimization far easier than raw logging.

In production, log every prompt sent and every response received (with appropriate PII redaction). These logs are invaluable for debugging failures, detecting quality regressions, computing cost analytics, and building eval datasets from real traffic.

Many LLMs are proficient at generating code in various programming languages, debugging, explaining code snippets, and even translating code between languages. However, generated code always requires careful review.

If a complex prompt isn't working, simplify it. Remove parts one by one to identify which section is causing the issue.

If LLM-generated code has errors, provide the error message back to the LLM and ask it to debug and fix the code. Iterate until it works.

Ask the LLM to explain your problem back to you in different words. This often reveals assumptions or gaps in your problem description.