FDE-024study-topics/ai-evaluation-and-quality.mdUPDATED: 06/18/2026
AI Evaluation And Quality Measurement
Topic
Name: AI evaluation and quality measurement
Why it matters for FDE roles: Customers need to know whether an AI workflow is reliable enough to use, not just whether it worked in a demo.
Plain-English Definition
AI evaluation is the practice of testing model outputs against expected behavior, acceptance criteria, source material, and real user needs.
Where It Shows Up
- Job listing signal: evals, testing, quality, AI reliability, production AI, model performance.
- Portfolio project connection: Ops Knowledge Copilot needs sample records and expected outputs for summaries, citations, and next actions.
- Real customer scenario: A team wants to know whether an AI assistant correctly summarizes tickets before users rely on it.
Core Concepts
- Golden examples: representative inputs with expected outputs or review criteria.
- Regression tests: repeatable checks that catch quality drops after prompt, model, or retrieval changes.
- Human review: subject-matter experts rate usefulness, correctness, and risk.
- Acceptance criteria: explicit standards for "good enough" in the customer workflow.
- Error categories: hallucination, missing evidence, wrong source, bad formatting, unsafe action, low usefulness.
Failure Modes
- Judging quality from a few cherry-picked examples.
- Optimizing for model fluency instead of workflow usefulness.
- No way to compare prompt or model changes.
- No human reviewer who understands the domain.
- Treating evals as one-time launch work instead of an ongoing feedback loop.
Tiny Practice Task
Create 10 sample operational records with expected summary, citation, uncertainty, and next-action criteria.
Interview Language
One sentence I could say in an interview:
I try to define AI quality in workflow terms: did the output use the right sources, expose uncertainty, follow the schema, and help the user make a better decision?
Relevant work experience for this topic.