Evaluation and testing
Measure whether an AI feature works, with test sets, metrics and reviews.
21tutors
12teachers
1free to start
$4 to $14per paid lesson
Evaluation and testing tutors
21 tutors
Regression Testing Your Prompts
Regression Testing Your Prompts
Catch quality drops before users do by running an eval suite on every prompt, model or setting change.42 lessonsSampleGonzalo Ibarra$7Classifying Text with an LLM APIClassifying Text with an LLM API
Sort tickets, reviews or messages into categories with a model, and measure how accurate it really is.39 lessonsSampleGreta Lindqvist$4Take Home Assignments and System Design for AI RolesTake Home Assignments and System Design for AI Roles
Handle AI take homes and design rounds with clear scoping, baselines, evaluation and trade offs30 lessonsSampleYohannes Tesfaye$13CI and testing for ML projectsCI and testing for ML projects
Add fast automated checks that catch broken data, code and models before they reach usersMalik Brennan$8Golden Answers and Reference ChecksGolden Answers and Reference Checks
Write reference answers experts agree on and compare model output to them with the right matching method.Gonzalo Ibarra$5Human Review That ScalesHuman Review That Scales
Set up human review of AI outputs that is consistent, affordable and feeds back into better systems.Gonzalo Ibarra$5A/B Testing LLM Features in ProductionA/B Testing LLM Features in Production
Run fair online experiments on prompts, models and LLM features and read the results without fooling yourself.Gonzalo Ibarra$10Evaluating generated textEvaluating generated text
Measure the quality of generated text with metrics, people and model judges, and know each one's limitsMateo Rojas$8Evaluating AI Learning Tools Before You BuyEvaluating AI Learning Tools Before You Buy
Judge AI edtech on evidence, safety, data, accessibility and real classroom fit before you commitValentin Moreau$10Teachers who teach Evaluation and testing
They wrote the lesson plans these tutors follow.
Tariq Haddad
Risk, vendors, data and support: the unglamorous work that makes AI safe to rely onvendor trials, data readiness, AI risk and compliance basics9 tutorsSampleNeha Varadan
Fine tuning with judgment: when to do it, how to do it well, and how to know it workedfine tuning strategy, dataset preparation, LoRA and parameter efficient methods9 tutorsSampleValentin Moreau
Lecturer and learning designer for universities, workplace training and coaching practicesHigher education teaching, assessment design, instructional design9 tutorsSampleSanjana Rao
Product management for AI features: deciding, specifying, testing and pricing them wellAI product management, product requirements, UX of AI features9 tutorsSampleFelix Brandt
Numerical Python and code quality for data and AI projects that have outgrown a single notebookNumPy, pandas, plotting9 tutorsSampleFarid Haddad
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observabilitytoken counting, cost control, prompt caching9 tutorsSampleMore in Building with AI
- Python for AI The Python you need for AI work: data, notebooks, libraries and small scripts. 21 tutors
- Building with LLM APIs Call language models from your own code: requests, streaming, tools and costs. 30 tutors
- RAG and search Connect a model to your own documents with embeddings, search and retrieval. 21 tutors
- AI agents Models that plan, use tools and act in steps, and how to keep them on track. 31 tutors
- AI automation Hand repetitive work to AI with workflows, triggers and simple integrations. 25 tutors
- AI coding assistants Write, read and debug code faster with an AI pair programmer. 30 tutors