Skip to content
L-SYSTEM

intermediate

Generative AI for Software Testing: A Practitioner's Course

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.

Path through the course · 21 units

solid line — the way is open · dashed — closed for nowView: map · steps

2 · closed

The Model You Picked Costs You Twice

~0.6 h

3 · closed

The Screenshot Your Model Cannot Read

~0.6 h

4 · closed

"Make Me Some Test Cases"

~1 h

5 · closed

Three Techniques, and Where Each Breaks

~1 h

6 · closed

Finding the Holes in the Requirement

~0.8 h

7 · closed

From Acceptance Criteria to Test Cases

~0.8 h

8 · closed

Gherkin at Scale

~0.8 h

9 · closed

The Regression Suite That Heals Itself

~0.8 h

10 · closed

Reading a Failed Nightly Run

~0.7 h

11 · closed

Numbers the Partner Will Read

~0.6 h

12 · closed

Choose the Technique Before You Write

~0.7 h

13 · closed

Proving the Output Is Good Enough

~0.8 h

14 · closed

It Sounded Right and It Was Wrong

~0.8 h

15 · closed

Turning the Temperature Down

~0.6 h

16 · closed

Client Data Does Not Leave the Firm

~0.8 h

17 · closed

Who Signs Off That This Was Legal

~0.8 h

18 · closed

The Model Has Never Seen Your Test Cases

~0.9 h

19 · closed

The Agent That Filed Forty Bad Defects

~0.7 h

20 · closed

When Prompting Is Not Enough

~0.8 h

21 · closed

Shadow AI in an Audit Firm

~0.8 h

final

Exam

closed

The path in words
  • Stage 1 · Same Prompt, Different Test Suitecan start; ~0.6 h
  • Stage 2 · The Model You Picked Costs You Twiceclosed; all of the previous needed (u01, 0/1); ~0.6 h
  • Stage 3 · The Screenshot Your Model Cannot Readclosed; all of the previous needed (u02, 0/1); ~0.6 h
  • Stage 4 · "Make Me Some Test Cases"closed; all of the previous needed (u03, 0/1); ~1 h
  • Stage 5 · Three Techniques, and Where Each Breaksclosed; all of the previous needed (u04, 0/1); ~1 h
  • Stage 6 · Finding the Holes in the Requirementclosed; all of the previous needed (u05, 0/1); ~0.8 h
  • Stage 7 · From Acceptance Criteria to Test Casesclosed; all of the previous needed (u06, 0/1); ~0.8 h
  • Stage 8 · Gherkin at Scaleclosed; all of the previous needed (u07, 0/1); ~0.8 h
  • Stage 9 · The Regression Suite That Heals Itselfclosed; all of the previous needed (u08, 0/1); ~0.8 h
  • Stage 10 · Reading a Failed Nightly Runclosed; all of the previous needed (u09, 0/1); ~0.7 h
  • Stage 11 · Numbers the Partner Will Readclosed; all of the previous needed (u10, 0/1); ~0.6 h
  • Stage 12 · Choose the Technique Before You Writeclosed; all of the previous needed (u11, 0/1); ~0.7 h
  • Stage 13 · Proving the Output Is Good Enoughclosed; all of the previous needed (u12, 0/1); ~0.8 h
  • Stage 14 · It Sounded Right and It Was Wrongclosed; all of the previous needed (u13, 0/1); ~0.8 h
  • Stage 15 · Turning the Temperature Downclosed; all of the previous needed (u14, 0/1); ~0.6 h
  • Stage 16 · Client Data Does Not Leave the Firmclosed; all of the previous needed (u15, 0/1); ~0.8 h
  • Stage 17 · Who Signs Off That This Was Legalclosed; all of the previous needed (u16, 0/1); ~0.8 h
  • Stage 18 · The Model Has Never Seen Your Test Casesclosed; all of the previous needed (u17, 0/1); ~0.9 h
  • Stage 19 · The Agent That Filed Forty Bad Defectsclosed; all of the previous needed (u18, 0/1); ~0.7 h
  • Stage 20 · When Prompting Is Not Enoughclosed; all of the previous needed (u19, 0/1); ~0.8 h
  • Stage 21 · Shadow AI in an Audit Firmclosed; all of the previous needed (u20, 0/1); ~0.8 h

Programme · 21 units

1Same Prompt, Different Test Suite

Explain why an LLM returns different output for identical input

~0.6 hcan start
2The Model You Picked Costs You Twice

Distinguish foundation, instruction-tuned and reasoning LLMs

~0.6 hclosed
3The Screenshot Your Model Cannot Read

Write and execute a prompt combining a wireframe and a user story for a test task

~0.6 hclosed
4"Make Me Some Test Cases"

Give examples of the six components of a structured prompt for a test task

~1 hclosed
5Three Techniques, and Where Each Breaks

Differentiate prompt chaining, few-shot prompting and meta prompting

~1 hclosed
6Finding the Holes in the Requirement

Apply generative AI to identify defects in the test basis

~0.8 hclosed
7From Acceptance Criteria to Test Cases

Apply generative AI to generate test cases from acceptance criteria

~0.8 hclosed
8Gherkin at Scale

Use few-shot prompting to produce output in a fixed house format

~0.8 hclosed

Show the remaining 13 units