AI LLM Testing
Quality engineering for AI systems — testing non-deterministic LLM and RAG outputs the way QA tests deterministic code.
One-time course fee
Who should take this?
Security, QA, engineering, governance, and risk professionals working with model-enabled applications.
Capability developed
Test, threat-model, evaluate, and harden AI systems before and after release.
Professional application
AI quality assurance, security review, red teaming, governance, or release-readiness work.
What’s inside this track
- 1Why AI Testing Is Different
Build: A test plan adapted for a non-deterministic AI feature.
Evidence: Stops applying deterministic QA logic to a probabilistic system.
- 2Evaluation Metrics & Golden Datasets
Build: A golden dataset and eval harness for a real LLM feature.
Evidence: Can prove — with numbers — whether an AI feature actually works.
- 3Tooling: DeepEval & RAGAS
Build: Wire DeepEval / RAGAS into a CI pipeline.
Evidence: AI quality regressions get caught in CI, not by angry users.
- 4End-to-End AI Testing Project
Build: Capstone: a complete AI testing project covering both UI automation and output validation.
Evidence: Can run AI QA the way a real QA team would expect it delivered.
What you get
What you will own
Not a promise — a concrete list of what you personally build and can show a buyer, pulled from this course’s real curriculum.
- A working project: A test plan adapted for a non-deterministic AI feature.
- A working project: A golden dataset and eval harness for a real LLM feature.
- A working project: Wire DeepEval / RAGAS into a CI pipeline.
- A working project: Capstone: a complete AI testing project covering both UI automation and output validation.
- A completed capstone: AI Feature End-to-End QA Validation, scored across multiple dimensions
- A portfolio entry documenting how you built it, not just that you finished
- Verified skill evidence — tied to real work, not a self-rating
What you’ll prove — and own — by the end
Completion isn’t the finish line. This is what turns the course into evidence you can actually use.
Personalised from day one
A short entry diagnostic flags which modules to move through quickly and which to slow down on — the full curriculum stays available either way.
Evidence, not checkmarks
Skills track through a real mastery ladder — module completed, exercise practiced, project shipped — not just “lesson viewed.”
Your Final real-world project, AI-defended
AI Feature End-to-End QA Validation — submit real work, defend your decisions to an AI interviewer, get scored feedback across multiple dimensions.
A portfolio you can use today
Turn a finished project into a case study, resume bullets, and an interview story — drafted from your real work, yours to review before you use it anywhere.
Connect this course to practical work
See how Ropes links a skill to a business problem, portfolio proof, a bounded service, and responsible client delivery. No client or income outcome is guaranteed.
Earnings potential
A simple illustration of how freelancers commonly structure retainer pricing once they’re taking on client work.
Security review retainer
Illustrative example — your results will vary based on niche, effort, and client base.
Industry snapshot
Real, sourced research — current tools, market signal, and named case studies, each with a citation. Not marketing copy.