1 · can start
Same Prompt, Different Test Suite
~0.8 h

A hands-on course on using large language models across the test process: prompting for test analysis and design, evaluating what the model gives back, containing its risks, and rolling it out in an organisation that handles client data. Built around a professional-services engagement, where confidentiality and documented review are not optional. Assumes working knowledge of test design, test levels and defect management.
Material and exam: English · your assistant leads the lesson
Your assistant takes the course material from our server and leads the lesson in your language.
The exam is taken on the site, without an assistant. Exam questions are not translated: this is decided for every course.
solid line — the way is open · dashed — closed for nowView: map · steps
1 · can start
Same Prompt, Different Test Suite
~0.8 h
2 · closed
The Model You Picked Costs You Twice
~0.7 h
opens after “Same Prompt, Different Test Suite”
3 · closed
The Screenshot Your Model Cannot Read
~0.7 h
opens after “The Model You Picked Costs You Twice”
4 · closed
"Make Me Some Test Cases"
~1.1 h
opens after “The Screenshot Your Model Cannot Read”
5 · closed
Three Techniques, and Where Each Breaks
~1.1 h
opens after “"Make Me Some Test Cases"”
6 · closed
Finding the Holes in the Requirement
~1 h
opens after “Three Techniques, and Where Each Breaks”
7 · closed
From Acceptance Criteria to Test Cases
~1 h
opens after “Finding the Holes in the Requirement”
8 · closed
Gherkin at Scale
~0.9 h
opens after “From Acceptance Criteria to Test Cases”
9 · closed
The Regression Suite That Heals Itself
~0.9 h
opens after “Gherkin at Scale”
10 · closed
Reading a Failed Nightly Run
~0.8 h
opens after “The Regression Suite That Heals Itself”
11 · closed
Numbers the Partner Will Read
~0.7 h
opens after “Reading a Failed Nightly Run”
12 · closed
Choose the Technique Before You Write
~0.8 h
opens after “Numbers the Partner Will Read”
13 · closed
Proving the Output Is Good Enough
~0.9 h
opens after “Choose the Technique Before You Write”
14 · closed
It Sounded Right and It Was Wrong
~0.9 h
opens after “Proving the Output Is Good Enough”
15 · closed
Turning the Temperature Down
~0.7 h
opens after “It Sounded Right and It Was Wrong”
16 · closed
Client Data Does Not Leave the Firm
~1 h
opens after “Turning the Temperature Down”
17 · closed
Who Signs Off That This Was Legal
~0.9 h
opens after “Client Data Does Not Leave the Firm”
18 · closed
The Model Has Never Seen Your Test Cases
~1.1 h
opens after “Who Signs Off That This Was Legal”
19 · closed
The Agent That Filed Forty Bad Defects
~0.8 h
opens after “The Model Has Never Seen Your Test Cases”
20 · closed
When Prompting Is Not Enough
~0.9 h
opens after “The Agent That Filed Forty Bad Defects”
21 · closed
Shadow AI in an Audit Firm
~0.9 h
opens after “When Prompting Is Not Enough”
final · in the browser, without the assistant
Exam
closed · enrol on the course
top to bottom · a unit opens when its conditions are metView: map · steps
~0.8 h
1 · can start
Same Prompt, Different Test Suite
~0.8 h
~0.7 h
2 · closed · opens after “Same Prompt, Different Test Suite”
The Model You Picked Costs You Twice
~0.7 h
~0.7 h
3 · closed · opens after “The Model You Picked Costs You Twice”
The Screenshot Your Model Cannot Read
~0.7 h
~1.1 h
4 · closed · opens after “The Screenshot Your Model Cannot Read”
"Make Me Some Test Cases"
~1.1 h
~1.1 h
5 · closed · opens after “"Make Me Some Test Cases"”
Three Techniques, and Where Each Breaks
~1.1 h
~1 h
6 · closed · opens after “Three Techniques, and Where Each Breaks”
Finding the Holes in the Requirement
~1 h
~1 h
7 · closed · opens after “Finding the Holes in the Requirement”
From Acceptance Criteria to Test Cases
~1 h
~0.9 h
8 · closed · opens after “From Acceptance Criteria to Test Cases”
Gherkin at Scale
~0.9 h
~0.9 h
9 · closed · opens after “Gherkin at Scale”
The Regression Suite That Heals Itself
~0.9 h
~0.8 h
10 · closed · opens after “The Regression Suite That Heals Itself”
Reading a Failed Nightly Run
~0.8 h
~0.7 h
11 · closed · opens after “Reading a Failed Nightly Run”
Numbers the Partner Will Read
~0.7 h
~0.8 h
12 · closed · opens after “Numbers the Partner Will Read”
Choose the Technique Before You Write
~0.8 h
~0.9 h
13 · closed · opens after “Choose the Technique Before You Write”
Proving the Output Is Good Enough
~0.9 h
~0.9 h
14 · closed · opens after “Proving the Output Is Good Enough”
It Sounded Right and It Was Wrong
~0.9 h
~0.7 h
15 · closed · opens after “It Sounded Right and It Was Wrong”
Turning the Temperature Down
~0.7 h
~1 h
16 · closed · opens after “Turning the Temperature Down”
Client Data Does Not Leave the Firm
~1 h
~0.9 h
17 · closed · opens after “Client Data Does Not Leave the Firm”
Who Signs Off That This Was Legal
~0.9 h
~1.1 h
18 · closed · opens after “Who Signs Off That This Was Legal”
The Model Has Never Seen Your Test Cases
~1.1 h
~0.8 h
19 · closed · opens after “The Model Has Never Seen Your Test Cases”
The Agent That Filed Forty Bad Defects
~0.8 h
~0.9 h
20 · closed · opens after “The Agent That Filed Forty Bad Defects”
When Prompting Is Not Enough
~0.9 h
~0.9 h
21 · closed · opens after “When Prompting Is Not Enough”
Shadow AI in an Audit Firm
~0.9 h
closed · enrol on the course
final · in the browser, without the assistant
Exam
| 1 | Same Prompt, Different Test Suite Explain why an LLM returns different output for identical input | ~0.8 h | can start |
| 2 | The Model You Picked Costs You Twice Distinguish the foundation, instruction-tuned and reasoning stages of LLM training | ~0.7 h | closed |
| 3 | The Screenshot Your Model Cannot Read Write and execute a prompt combining a wireframe and a user story for a test task | ~0.7 h | closed |
| 4 | "Make Me Some Test Cases" Give examples of the six components of a structured prompt for a test task | ~1.1 h | closed |
| 5 | Three Techniques, and Where Each Breaks Differentiate prompt chaining, few-shot prompting and meta prompting | ~1.1 h | closed |
| 6 | Finding the Holes in the Requirement Apply generative AI to identify defects in the test basis | ~1 h | closed |
| 7 | From Acceptance Criteria to Test Cases Apply generative AI to generate test cases from acceptance criteria | ~1 h | closed |
| 8 | Gherkin at Scale Use few-shot prompting to produce output in a fixed house format | ~0.9 h | closed |