Specialisation roadmap
The GenAI Testing Roadmap
Generative systems break the assumption every test framework is built on: the same input no longer produces the same output. Testing them is not impossible — it just needs different instruments. This roadmap covers those instruments in the order they become useful.
The roadmap, stage by stage
Stage 1 — Understand the behaviour
Temperature, sampling and context windows determine how much a model is allowed to vary. Knowing the knobs is the prerequisite to testing the output.
- Temperature ranges: 0–0.3 factual, 0.7–1.0 creative
- Context window limits
- Deterministic vs probabilistic expectations
Stage 2 — Prompt regression
Prompts are code. Version them, and run a regression suite whenever they change — including multi-run consistency checks at fixed temperature.
Stage 3 — RAG pipeline validation
For retrieval-augmented systems, test the whole pipeline: did retrieval find the right documents, was the context relevant, and is the answer grounded in it?
- Retrieval accuracy
- Context relevance
- Grounding and citation checks
- Hallucination detection
Stage 4 — Responsible AI and drift
Ship with bias and fairness checks, then keep watching. Models, prompts and data sources all move under you after release.
Skills you'll gain
- Prompt stability testing
- RAG pipeline validation
- Hallucination and grounding checks
- Bias and fairness testing
- Drift monitoring
- API automation for model endpoints
Why it matters for your career
Very few testers can currently sign off a GenAI feature with evidence. Being one of them is the fastest differentiator available in QA right now.
Recommended learning path
GenAI Testing Specialist
Confidently test LLMs, agents, RAG pipelines, and MCP servers — the skills nobody else is teaching.
4 courses · ~18 hours · Intermediate → Advanced
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FAQ
How do you test something non-deterministic?
You test properties rather than exact strings: grounding, schema, safety, consistency across runs and behaviour at fixed temperature.
What tools do I need?
Python, an LLM API, and an evaluation harness. The courses build these up from scratch.
Is prompt engineering enough?
No. Prompting is authoring; testing is evidence. You need regression suites, not better wording alone.
Not sure where you fit on this roadmap?
Take the quiz for a personalised path, or ask Tia — she'll build a plan around your experience.