Evaluating & Measuring AI: Advanced

Learners define success, build evaluations, and measure AI value and adoption. The course covers defining success, building evals, human review and red-teaming, and ROI and adoption metrics. Participants finish able to tell whether an AI system is actually good - and prove it.

What this course covers

The course runs across 8 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Defining Success Metrics for AI Systems — Establish clear, quantifiable success criteria and alignment metrics tailored to specific business domains and AI use cases.
Designing and Implementing Automated Evals — Learn the methodology of building programmatic evaluation pipelines, including model-graded evals, heuristic checks, and semantic similarity.
Configuring Evaluation Frameworks in the Tool — Hands-on walk-through of setting up, running, and managing evaluation datasets directly within the platform interface.
Human-in-the-Loop Review Workflows — Design effective human evaluation interfaces, resolve annotator disagreement, and integrate human feedback loops into the continuous evaluation cycle.
Red-Teaming and Vulnerability Testing — Proactively identify system weaknesses, jailbreaks, and edge cases through adversarial testing methodologies and automated red-teaming scripts.
Analyzing Evaluation Runs and Debugging Failures — Interpret evaluation results, drill down into failure modes, and make data-driven decisions on prompt engineering or fine-tuning adjustments.
Measuring ROI and Adoption Metrics — Track system latency, API costs, user retention, and task-completion rates to quantify the tangible business value and operational ROI of your AI deployment.
Synthesizing Metrics for Stakeholder Reporting — Consolidate technical evaluations, human review scores, and financial ROI metrics into executive dashboards to justify production readiness.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Model Monitoring, AI Auditing, Measuring AI ROI, Model Evaluation & Selection, Red Teaming.

Model Monitoring
AI Auditing
Measuring AI ROI
Model Evaluation & Selection
Red Teaming

Who it is for

Builders, PMs, leaders. The material is pitched at advanced level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Evaluation track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario, code lab, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Evaluating & Measuring AI: Mastery — Benchmarking at Scale — The expert tier of AI evaluation: building eval suites that scale, using model graders without fooling yourself, and wiring evals into CI as regression gates. Learners leave with a production-grade eval pipeline.
Evaluating & Measuring AI: Essentials — Telling Good Answers from Bad — Learners will be able to judge an AI answer before they use it, instead of trusting how confident it sounds. The course covers quick quality checks - does it answer the actual question, are the facts checkable, would an expert nod - and comparing two answers to pick the better one. They leave with a short judging habit they apply to every answer that matters.
AI Governance & Risk — Stand up policy, risk, and oversight for AI
Production AI Engineering — Take AI from prototype to reliable production

Frequently asked questions

How long does this course take?

It runs roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-EVL.