#3 — “It Worked Yesterday, Failed Today”
📰 Medium · LLM
Debugging LLMs can be tricky, learn how to identify and fix issues when tests pass one day and fail the next
Action Steps
- Run the same test multiple times to verify inconsistency
- Check for changes in input data or environment
- Apply debugging techniques to identify the root cause of the issue
- Test with different LLM models or configurations to compare results
- Analyze logs and error messages to gain insights into the failure
- Compare model performance over time to detect potential drift
Who Needs to Know This
Machine learning engineers and developers working with LLMs can benefit from understanding the common pitfalls and debugging techniques to ensure consistent model performance
Key Insight
💡 LLMs can be sensitive to small changes in input or environment, leading to inconsistent results
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🤖 Did your LLM test pass yesterday but fail today? Don't blame the ghosts, debug it! 💻
Key Takeaways
Debugging LLMs can be tricky, learn how to identify and fix issues when tests pass one day and fail the next
Full Article
Pop quiz: you run the same test twice and get two different results. Is your code haunted? … No. It’s just an LLM. (Honestly, haunted… Continue reading on Medium »
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