Has this ever happened to you? You spent a weekend feeling like a genius because you chained together a few LLM prompts in Zapier, or maybe you built a "content generator" in your company’s internal tool like Glean. It worked perfectly for the first three tries. You showed your boss and they immediately promoted you. For the first time in your life, your father told you he was proud.
Then, on Tuesday, everything went awry: your system hallucinated a policy that doesn't exist, and a customer got a free Labubu. On Wednesday, it responded to a user in German (you are based in Ohio). By Thursday, you’re back to doing it manually because you can't trust the "magic" bot you built.
You are not entirely surprised to discover that your tool has some rough edges that need sorting out and your prompts are not entirely bulletproof. And thus, your journey into evals begins.
The TL;DR
- Evals are just "software testing" adapted for the fuzzy, probabilistic world of AI. They measure (and help you fix) when your system screws up.
- The most important part of evals is data analysis – you cannot measure and fix what you cannot identify. Look at your data!
- Complex "LLM as a Judge" setups seem sexy but will probably ruin your life; avoid them until you have exhausted simple keyword checks (assertions).
- If you don't measure your AI's performance systematically, you aren't building a tool; you're building a slot machine that occasionally pays out productivity.
