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Univelcity
QualityInt–Adv

AI Evals

Every serious AI team is bottlenecked on the same question: how do you know it's good enough? Evaluation is the discipline that turns "it seems to work" into evidence — and it's fast becoming one of the most sought-after skills in AI.

Who it's for

  • Engineers and PMs who need to prove an AI system is reliable.
  • QA, data, and analytics people moving into AI quality roles.
  • Anyone responsible for whether an AI product ships.

Prerequisite: comfort with data, and some exposure to building or shipping AI features.

What you'll build

A complete evaluation pipeline for a real AI system — dataset, harness, methods, and a reporting layer — that turns quality into evidence.

What you'll learn

  • Define what "good" means for an AI system in measurable terms.
  • Build evaluation datasets and harnesses that catch failures before users do.
  • Use human, automated, and model-based (LLM-as-judge) evaluation appropriately.
  • Detect regressions and drift, and build evals into a continuous pipeline.
  • Turn evaluation results into decisions engineers and PMs can act on.
  1. 01
    Why evals decide everything
    The role of evaluation in shipping AI, and what goes wrong without it.
  2. 02
    Defining quality
    From fuzzy goals to measurable criteria and rubrics.
  3. 03
    Building eval datasets
    Collecting, curating, and versioning the data you evaluate against.
  4. 04
    Methods of evaluation
    Human review, automated metrics, and LLM-as-judge — when to use each.
  5. 05
    Evals in the pipeline
    Regression testing, monitoring, and catching drift in production.
  6. 06
    From results to decisions
    Reporting, dashboards, and driving action. Capstone.
FORMAT

Live & hands-on

Live, expert-led, small cohort · 6 weeks · online · hands-on.
WALK AWAY

Certificate + proof of work

Univelcity AI Evals certificate + a portfolio-grade evaluation pipeline.
TAUGHT BY

Practitioners

TODO — instructor details.

Questions

Good to know.

Do I need to be an engineer?
Some technical comfort helps, but this is as much for PMs and QA leads as for engineers.
Does this pair with AI Engineering?
Yes. Evals are the other half of shipping. Many learners take them close together.
Why does this matter so much right now?
Reliable evaluation is what separates AI demos from AI products. Teams are hiring for exactly this.

Make AI reliable enough to ship.

Apply for the next cohort.

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