Applied AI / Forward Deployed Engineer · Evaluation & Quality · Lesson 1 of 4
Why evals are the #1 applied-AI skill
LLM outputs are non-deterministic and open-ended, so you cannot tell if a change helped by eyeballing a few examples. Evaluations (evals) are systematic tests that score model output against expectations. Teams that win at applied AI are the ones that measure.
- Without evals you're 'prompt-and-pray' — every change is a guess and regressions are invisible.
- Evals turn 'it feels better' into 'accuracy went 82% → 89% on 200 held-out cases.'
- They enable iteration speed: change prompt/model/retrieval, re-run the eval, keep what wins.
This is THE differentiatorWhen an interviewer asks 'how do you know your system is good?' the wrong answer is 'we tested it manually.' The right answer starts with 'I build an eval set from real examples and measure against it on every change.' Lead with evals.
◆ Lock it in
- You can't eyeball non-deterministic output — evals make quality measurable.
- Evals catch regressions and enable fast, safe iteration.
- 'How do you know it's good?' → lead with your eval strategy.
Feynman drill — say it out loudExplain to a PM why 'it looked good in my three tests' is not evidence the AI feature works.
Step 1 rate your confidence · Step 2 pick your answer
Why are evals especially critical for LLM systems (vs traditional deterministic software)?
Step 1 — how sure are you?