LLM Evals: How to Know Your AI Code Actually Works

When someone on your team decides to add an AI feature, like a code suggester that completes sentences, or a magical "fix this" for bugs, it's easy to get excited. The problem comes later: how do you know it's not suggesting nonsense or, worse, dangerous code? This is where LLM evaluations come in, tools designed to objectively measure how good your AI really is.

LLM evaluations for developer tools are designed to measure the effectiveness and safety of AI functionalities integrated into software. Basically, they tell you if the AI is useful, correct, and safe before it lands in an unsuspecting user's hands and causes trouble. Without them, we're flying blind, hoping for the best but often finding the worst.

Imagine an AI copilot suggesting code to you. If that copilot was poorly evaluated, it might propose outdated, insecure, or just plain wrong solutions. This not only wastes valuable developer time but also introduces potential security vulnerabilities that could be very costly. In 2023, many teams began integrating these systems to avoid embarrassing blunders.

Evaluating AI in a tool means subjecting it to a series of specific tests, just like any other piece of software. For example, you check if a code suggestion is syntactically correct, if it actually solves the problem, or if it introduces known vulnerabilities. It's not just about "does it work?" but "does it work well and responsibly?"

These tests are no walk in the park. They require clearly defining what "useful," "correct," and "safe" mean for each specific feature. Evaluating a model that answers questions is one thing; judging a model that manipulates critical code is another. We're not talking about choosing ice cream flavors here, but about software reliability. Do you really want to launch an AI feature without thoroughly testing it?