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AI AUTOMATION

Automate QA work with AI tools — faster tests, cleaner docs, better reports

Learn how to use modern AI tools like ChatGPT, Claude, and Cursor to speed up everyday QA tasks: test design, bug reporting, reproduction steps, documentation, API test generation, and workflow automation. This is not “theory” — you’ll build reusable prompt packs and mini-automation flows you can use every day.

Best for
Manual QA, automation QA, SDETs, career switchers
Outcome
AI prompt library + faster QA workflow + portfolio artifacts
Duration
2–4 weeks (fast + practical)
Support
Feedback on prompts + workflows
Enroll in AI Automation See curriculum
Tip: This course works great as an add-on to QA Basic/Advance/Automation.
What you will be able to do
  • Generate strong test ideas and coverage (without missing edge cases)
  • Turn requirements into test cases & checklists in minutes
  • Create “developer-friendly” bug reports with evidence and clarity
  • Use AI to debug and explain errors/logs faster
  • Generate API test scenarios + sample RestAssured/Postman tests
  • Build reusable prompt packs for your daily QA workflow
Portfolio result
Prompt Library • Test Case Generator • Bug Report Templates • QA Docs Pack

AI Toolkit (what we use)

ChatGPT
Test design, bug reporting, docs, role-play with dev/product.
Claude
Long requirements → structured test cases, clean summaries.
Cursor
AI inside IDE: refactor tests, generate code, explain errors.
Optional tools
Perplexity / Gemini / Copilot (how to choose and compare).
Docs workflow
Google Docs / Notion / Confluence style documentation.
QA tools
Jira, Postman, logs, DevTools — with AI assistance.
We also cover safety rules: how to avoid sharing sensitive data and how to anonymize examples.

Curriculum

Practical modules — every lesson gives you a reusable template.
Module 1 — AI foundations for QA
How to get reliable results (and avoid hallucinations).
  • Prompt structure: role + context + constraints + output
  • Asking for clarifying questions automatically
  • Verification: checklists for AI answers
Module 2 — Test cases & checklists generator
From requirements → coverage fast.
  • Edge cases generator prompt
  • Risk-based prioritization prompt
  • Regression list builder prompt
Module 3 — Bug report “pro” templates
Clear steps, expected/actual, evidence.
  • Bug rewriting into Jira format
  • Severity/priority suggestion with reasoning
  • Reproduction + minimal steps
Module 4 — Debugging with AI
Logs, errors, console messages — explained fast.
  • “Explain this stack trace” workflow
  • Root cause hypotheses (ranked)
  • Next steps for QA to confirm
Module 5 — API testing with AI
Make API testing faster and more complete.
  • Generate API test scenarios from endpoints
  • Create Postman tests / sample collections
  • Generate RestAssured test skeletons
Module 6 — Cursor workflows for test code
AI in IDE: write/clean tests faster.
  • Refactor tests to Page Objects
  • Improve waits/locators (stability)
  • Generate reusable utilities (config, helpers)
Module 7 — QA documentation with AI
Professional docs that look like real team work.
  • Test plan generator
  • Release notes / QA summary generator
  • Stakeholder-friendly “What’s risky?” report
Module 8 — Personal AI system for QA
Turn templates into a daily routine.
  • Prompt library organization
  • Reusable “one-click” outputs structure
  • Quality checklist for AI outputs
Final Project — AI QA Toolkit Pack
A deliverable you can reuse daily (and show in interviews).
  • Prompt library (20–40 prompts, categorized)
  • Test case generator template (requirements → tests)
  • Bug report “pro” template (Jira-ready)
  • QA summary / test plan templates

Included Prompt Templates (examples)

1) Requirements → Test Cases
Role: Senior QA Lead. Create test cases for the feature below. Output: table with Preconditions, Steps, Expected, Priority, Type. Add: negative + edge cases + validations.
2) Bug Report Rewriter
Rewrite this issue into a Jira bug: Title, Environment, Steps, Expected, Actual, Severity, Evidence suggestions, Possible root cause (optional).
3) Log/Stacktrace Explainer
Explain the error. Give 3 root cause hypotheses (ranked), and QA steps to confirm each. Then propose a minimal reproducible test.
4) API Coverage Builder
Based on endpoints list, create API test scenarios: auth, validation, negative, rate limits, pagination, data integrity. Output as checklist + priority.
These are examples — you’ll receive a full structured library in the final project.

Format & Support

Fast lessons
Short lessons + immediately usable prompt templates.
Hands-on
You practice on real requirements, bugs, logs, and endpoints.
Feedback
Optional review: your prompts, structure, and quality rules.
Reusable system
You finish with a personal AI workflow you can reuse daily.

Enroll in AI Automation

Pay & Enroll Questions?

FAQ

Do I need automation skills for this course?
No. This course improves workflow for both manual and automation QA.
Is this course about building AI bots?
No — it’s about using AI tools to automate your QA tasks and speed up daily work (practical).
Will I learn Cursor?
Yes — we cover realistic Cursor workflows to generate/refactor tests and debug faster.
Can I use this at work safely?
We cover safety rules: anonymizing data, avoiding secrets, and keeping sensitive info private.
What will I have at the end?
A complete AI QA Toolkit Pack: prompt library + templates + your workflow rules.
Made on
Tilda