Unit
AI Workflows for Engineering and QA
AI Workflows for Engineering and QA helps software teams use LLMs, copilots, and coding agents to improve coding, debugging, review, testing, and release readiness without sacrificing engineering judgement, code quality, or accountability.
What you'll learn
AI in the Engineering Workflow
- Identify engineering workflows where AI tools can add value without compromising quality
- Identify QA workflows where AI tools can add value without compromising confidence
- Explain the role of human judgement in reviewing AI-assisted engineering outputs
- Distinguish effective AI-assisted engineering from shallow AI usage
AI-Assisted Coding and Debugging
- Explain how AI tools can support code generation in software development
- Explain how AI tools can support debugging activities
- Explain how AI tools can support refactoring activities
- Describe methods to critically review AI-generated code snippets
- Describe methods to validate AI-generated code before use in delivery workflows
AI-Assisted Code Review and Engineering Quality
- Explain how AI can support code review preparation
- Identify patterns where AI can help detect quality issues in code
- Recognise risks introduced by accepting AI-generated code without review
- Describe practices that preserve code quality when using AI tools
- Explain accountability expectations for AI-assisted engineering outputs
AI-Assisted Testing and QA
- Explain how AI can assist in generating test cases
- Explain how AI can assist in maintaining QA artefacts
- Identify techniques to evaluate AI-generated test cases for coverage
- Identify techniques to evaluate AI-generated test cases for edge cases
- Outline approaches to review AI-assisted QA outputs before release checks
Release Readiness and Delivery Confidence
- Explain how AI can support release readiness checks
- Identify AI-assisted methods for finding gaps in test coverage before release
- Summarise how AI can support defect analysis and quality trend review
- Describe review practices that maintain test confidence when using AI tools
Engineering Habits, Pitfalls, and Team Practice
- Identify common pitfalls that create rework when using AI in engineering workflows
- Identify common pitfalls that create rework when using AI in QA workflows
- Summarise strategies for integrating AI tools into existing engineering workflows responsibly
- Summarise strategies for integrating AI tools into existing QA workflows responsibly
- Outline methods to foster effective AI-assisted engineering habits across technical teams
