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QA engineer reviewing AI-generated test ideas against requirements risks and human checks
SQA, Test Automation & SDET · Course #054

Generative AI for QA Engineers

Designed for applied learning, Generative AI for QA Engineers shows how to use generative AI as a controlled assistant for analysis, test design, data, coding and reporting while preserving human accountability. It combines generative ai capabilities and limits in software testing with qa use-case selection and measurable success criteria, then extends the work through prompt structure. Learners compare prompt strategies, provide grounded context, review generated tests and code, detect hallucinations and bias, protect sensitive information and document where AI influenced a quality decision. Participants examine both successful and weak examples, build reusable working habits and consider where human review is essential. They create a reusable QA workflow with review gates and evaluation criteria rather than relying on plausible-looking output.

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Overview

Designed for applied learning, Generative AI for QA Engineers shows how to use generative AI as a controlled assistant for analysis, test design, data, coding and reporting while preserving human accountability. It combines generative ai capabilities and limits in software testing with qa use-case selection and measurable success criteria, then extends the work through prompt structure. Learners compare prompt strategies, provide grounded context, review generated tests and code, detect hallucinations and bias, protect sensitive information and document where AI influenced a quality decision. Participants examine both successful and weak examples, build reusable working habits and consider where human review is essential. They create a reusable QA workflow with review gates and evaluation criteria rather than relying on plausible-looking output.

Course Highlights

QA-specific generative AI workflows

Grounded prompt and context design

Human review and output evaluation

Test-data and code assistance

Privacy, bias and hallucination controls

Modules & Curriculum

Generative AI capabilities and limits in software testing

QA use-case selection and measurable success criteria

Prompt structure: context, task, constraints and evidence

Requirement and risk analysis with grounded context

Generating and diversifying scenarios and test data; assisting automation code without surrendering code review

Defect analysis, summarisation and stakeholder reporting

Hallucination, bias, privacy and security risks

Output evaluation, human gates and audit trail; responsible QA workflow and adoption experiment

Learning Outcomes

  • Select QA tasks where generative AI can add measurable value.
  • Construct grounded prompts with context, constraints and output criteria.
  • Review generated scenarios, data and code for omissions and false assumptions.
  • Evaluate outputs for relevance, coverage, consistency and safety.
  • Protect sensitive project information during AI-assisted work.

Ready to Build Your Future-Ready Skills?

Use generative AI to expand QA work while keeping evidence and judgement visible.

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