Testing and Evaluating AI Outputs training course

Why AI testing is different:

non-determinism, semantic correctness, and exactly where assert statements break down

Defining your rubric:

a workshop to build shared scoring criteria for accuracy, tone, helpfulness, and safety

Test dataset construction:

sourcing real prompts from production logs, writing expected outputs, and covering the important edge cases

LLM-as-judge setup:

configuring a second model to score outputs automatically and understanding when to trust that score

 

 

 

Structured output validation:

JSON schema checks, field-level assertions, and format regression tests that run in milliseconds

Hallucination detection lab:

reference matching techniques, factual grounding checks, and a confidence scoring approach you can implement today

Regression suite build:

running your full evaluation set on every prompt change and alerting when scores drop below threshold

Metrics dashboard:

tracking pass rates, average quality scores, and trends over time in a lightweight tool

 

 

CI/CD integration: adding evaluation runs to your existing pipeline and configuring deploy blocks on quality failures

Stakeholder reporting: translating evaluation scores into plain-language quality summaries that non-technical audiences can act on