The original title is "LLM Evals For Developer Tools: Useful, Correct, Safe"

Short summary

A practical framework for evaluating LLM features in developer tools along three axes: correctness (does the code compile and pass tests), usefulness (does the artifact actually help the user), and safety (did the agent leak secrets or run destructive commands). Developer tool evals differ from chatbot evals because outputs are executable artifacts with binary pass/fail outcomes, but failure modes are sharper with larger blast radius. The article argues every honest eval suite must measure all three axes, not just correctness.

• •Split dev-tool LLM evals into correctness, usefulness, and safety — each requires different techniques and stakeholders
• •Developer tools can leverage ground-truth execution (tests, builds, linters) far more than chatbot teams can
• •Safety is underweighted until it bites: agents with shell access can leak secrets, run destructive commands, or fall for prompt injection

Generated with AI, which can make mistakes.