LLM Evals For Developer Tools: Useful, Correct, Safe
📰 Dev.to AI
Learn to evaluate LLM features in developer tools to ensure they are useful, correct, and safe in real-world scenarios
Action Steps
- Build a test suite for your LLM feature using real-world codebases
- Run evaluations on your LLM feature regularly to track its performance
- Configure metrics to measure the usefulness, correctness, and safety of your LLM feature
- Test your LLM feature with different user inputs and edge cases
- Apply evaluation results to refine and improve your LLM feature
Who Needs to Know This
Developers and product managers can benefit from this knowledge to improve the reliability and effectiveness of LLM-powered features in their tools
Key Insight
💡 Evaluations are crucial to bridge the gap between demo success and real-world effectiveness of LLM features
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🤖 Evaluate your LLM features to ensure they're useful, correct, and safe! 🚀
Key Takeaways
Learn to evaluate LLM features in developer tools to ensure they are useful, correct, and safe in real-world scenarios
Full Article
Someone on your team built an LLM feature. Maybe it's an inline code-suggest. Maybe it's a "fix this PR comment" button. Maybe it's a full agent that opens pull requests on its own. The demo worked. The screenshots were good. You shipped it. Now a real user gives it a real codebase, and you have no idea whether it's getting better or worse week to week. That gap, between "it worked in the demo" and "we can prove this is improving," is what evals are for. And in 2026 we are still
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