AI for QA Engineers — Curriculum & Syllabus | CyberIntel AI Academy
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AI for QA Engineers

In-person (Chantilly) or live online Intermediate ·$1,400

Use AI for test design, test case generation, automation support, bug analysis, test data, and QA productivity.

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No payment now — we’ll email the next cohort dates and pricing for both formats.

// who it's for

Who this program is for

  • QA engineers, manual testers, and SDETs
  • Test leads modernizing their team's workflow
  • Automation engineers adding AI to their toolkit
// what you'll learn

What you will learn

  • Use AI to design tests and close coverage gaps
  • Generate and maintain test cases and test data
  • Apply AI to automation, debugging, and flaky-test analysis
  • Analyze defects and write clearer, faster bug reports
  • Integrate AI into the QA workflow responsibly and with governance

An instructor-led program delivered in person in Chantilly or live online. Sessions are hands-on and discussion-driven, with practical exercises throughout so QA professionals apply each technique to realistic testing work alongside their peers.

// syllabus

Curriculum

The full program syllabus — expand any chapter to see its lesson-by-lesson detail.

01AI Foundations for Quality EngineeringWhere AI fits in modern QA, and how to use it without losing rigor.
  • 1.1Why AI matters for modern QA
    The shift AI brings to quality work.
  • 1.2What LLMs can and cannot do for testing
    Realistic strengths and limits for QA.
  • 1.3The AI-assisted testing landscape
    Tools and categories worth knowing.
  • 1.4Setting up AI tools for QA work
    Configure a practical QA toolkit.
  • 1.5Strengths and risks of AI in quality work
    Benefits balanced against the risks.
  • 1.6Data privacy when testing with AI
    Protect sensitive data and systems.
  • 1.7Where AI fits in the testing lifecycle
    Map AI to each testing phase.
  • 1.8Human-in-the-loop quality control
    Keep testers in control of decisions.
  • 1.9Measuring AI impact on QA productivity
    Track real gains, not hype.
  • 1.10Common pitfalls and false confidence
    Avoid trusting AI output blindly.
  • 1.11Building an AI-assisted QA mindset
    Adopt AI as a tool, not a crutch.
  • 1.12Team norms for responsible AI in QA
    Agree on how the team uses AI.
02Prompting for Testing WorkPrompting patterns tuned for requirements, coverage, and test artifacts.
  • 2.1Prompting fundamentals for testers
    The core prompt skills for QA.
  • 2.2Describing features and requirements to AI
    Give the model the right context.
  • 2.3Prompts for test ideas and coverage
    Generate broad, useful test ideas.
  • 2.4Asking for structured test artifacts
    Get output you can use directly.
  • 2.5Role prompting as a QA expert
    Frame the model as a senior tester.
  • 2.6Few-shot prompting with existing tests
    Show examples to match your style.
  • 2.7Controlling output format for test tools
    Produce import-ready formats.
  • 2.8Iterating prompts for better coverage
    Refine to find more cases.
  • 2.9Grounding prompts in real requirements
    Anchor output to actual specs.
  • 2.10Reusable QA prompt templates
    Standardize prompts across the team.
  • 2.11Debugging weak or vague AI output
    Fix prompts that miss the mark.
  • 2.12Building a shared QA prompt library
    Organize prompts the team can reuse.
03AI-Assisted Test DesignTurn requirements into well-structured, risk-aware test conditions.
  • 3.1Turning requirements into test conditions
    Translate specs into testable items.
  • 3.2Equivalence partitioning with AI
    Group inputs to reduce redundancy.
  • 3.3Boundary value analysis support
    Find the edges that break things.
  • 3.4Decision tables and combinations
    Cover rule-based logic thoroughly.
  • 3.5State transition test ideas
    Test flows across system states.
  • 3.6Risk-based test prioritization
    Focus effort where risk is highest.
  • 3.7Exploratory testing charters with AI
    Design focused exploration sessions.
  • 3.8Identifying edge cases you missed
    Surface gaps in your thinking.
  • 3.9Negative and abuse-case testing
    Test for misuse and failure paths.
  • 3.10Acceptance criteria to test mapping
    Tie tests back to criteria.
  • 3.11Reviewing coverage gaps with AI
    Find what your suite misses.
  • 3.12Validating AI-suggested test design
    Verify before you adopt suggestions.
04Test Case Generation with AIGenerate, structure, and maintain test cases that stay traceable.
  • 4.1Generating test cases from user stories
    Go from story to cases fast.
  • 4.2Structuring steps, data, and expected results
    Write complete, usable cases.
  • 4.3Positive and negative case sets
    Cover happy and unhappy paths.
  • 4.4Generating Gherkin/BDD scenarios
    Produce behavior-driven scenarios.
  • 4.5Parameterized and data-driven cases
    Scale cases across data sets.
  • 4.6Traceability to requirements
    Keep cases linked to specs.
  • 4.7Reducing duplicate and redundant cases
    Trim overlap from your suite.
  • 4.8Reviewing AI-generated cases for quality
    Check generated cases carefully.
  • 4.9Maintaining cases as features change
    Keep cases current over time.
  • 4.10Exporting cases to your test tools
    Move cases into your tooling.
  • 4.11Prioritizing generated cases
    Decide what to run first.
  • 4.12A test-generation mini project
    Practice the full generation flow.
05AI for Test AutomationUse AI to write, refactor, and debug automation while keeping it maintainable.
  • 5.1AI in the automation workflow
    Where AI helps across automation.
  • 5.2Generating automation code from cases
    Turn cases into runnable scripts.
  • 5.3Page objects and locators with AI help
    Build resilient UI automation.
  • 5.4Writing assertions and validations
    Verify the right outcomes.
  • 5.5Refactoring and cleaning test code
    Keep automation readable.
  • 5.6Debugging failing automated tests
    Diagnose failures faster.
  • 5.7Reducing flaky tests with AI analysis
    Find and fix instability.
  • 5.8Framework selection and setup guidance
    Choose and configure tools wisely.
  • 5.9Reviewing AI-generated automation
    Audit generated code for quality.
  • 5.10Keeping automation maintainable
    Prevent automation debt.
  • 5.11Limits of AI-generated test code
    Know what AI gets wrong.
  • 5.12An automation assist walkthrough
    Apply the techniques end to end.
06Test Data Generation and ManagementCreate realistic, compliant test data for every scenario.
  • 6.1Why good test data matters
    The role of data in test quality.
  • 6.2Generating realistic synthetic data
    Create lifelike, safe data sets.
  • 6.3Edge-case and boundary data sets
    Cover extremes and limits.
  • 6.4Anonymizing and masking sensitive data
    Protect real data in testing.
  • 6.5Data for negative and error paths
    Test failure conditions properly.
  • 6.6Localized and internationalized data
    Cover locales and formats.
  • 6.7Large data sets and performance data
    Scale data for load testing.
  • 6.8Keeping test data consistent
    Maintain coherent data states.
  • 6.9Validating generated data quality
    Check data before using it.
  • 6.10Privacy and compliance for test data
    Stay within data regulations.
  • 6.11Managing data across environments
    Keep environments in sync.
  • 6.12A test-data generation exercise
    Build a realistic data set.
07Bug Analysis and Reporting with AIWrite clearer reports, triage faster, and analyze defect trends.
  • 7.1Writing clear, reproducible bug reports
    Report defects developers can act on.
  • 7.2Summarizing logs and stack traces
    Extract signal from noisy logs.
  • 7.3Suggesting probable root causes
    Get AI hypotheses to investigate.
  • 7.4Triaging and prioritizing defects
    Order bugs by impact and risk.
  • 7.5Reducing duplicate bug reports
    Spot and merge duplicates.
  • 7.6Improving reproduction steps
    Make defects easy to reproduce.
  • 7.7Drafting severity and impact notes
    Communicate impact clearly.
  • 7.8Linking bugs to tests and requirements
    Maintain traceability for defects.
  • 7.9Communicating defects to developers
    Frame issues for fast fixes.
  • 7.10Trend analysis across defects
    Find patterns in your bug data.
  • 7.11Verifying AI root-cause suggestions
    Confirm before acting on them.
  • 7.12A bug-reporting improvement lab
    Upgrade real reports hands-on.
08AI in API and Integration TestingDesign and validate API and integration tests with AI support.
  • 8.1Understanding API contracts with AI
    Read and reason about contracts.
  • 8.2Generating API test cases
    Produce thorough API tests.
  • 8.3Building requests and payloads
    Construct valid and edge payloads.
  • 8.4Validating responses and schemas
    Check structure and values.
  • 8.5Negative and security API cases
    Test misuse and abuse paths.
  • 8.6Mocking and stubbing with AI help
    Simulate dependencies for testing.
  • 8.7Integration scenario design
    Design realistic integration flows.
  • 8.8Reviewing OpenAPI/Swagger specs
    Analyze API specs for gaps.
  • 8.9Data flow and contract testing
    Verify data across boundaries.
  • 8.10Debugging API failures
    Diagnose failing API tests.
  • 8.11Limits and risks in API testing
    Know where AI can mislead.
  • 8.12An API testing mini project
    Apply the techniques end to end.
09AI for Performance, Accessibility, and Security TestingApply AI to non-functional testing without over-trusting the results.
  • 9.1AI-assisted performance test planning
    Plan realistic performance tests.
  • 9.2Interpreting performance results
    Make sense of metrics and trends.
  • 9.3Generating load scenarios
    Design representative load profiles.
  • 9.4Accessibility checks and WCAG guidance
    Test for accessible experiences.
  • 9.5Writing accessible test criteria
    Define clear a11y acceptance.
  • 9.6Basic security testing concepts for QA
    Security fundamentals for testers.
  • 9.7Common vulnerability awareness
    Recognize frequent weaknesses.
  • 9.8Generating security test ideas
    Brainstorm security-focused tests.
  • 9.9Reviewing results responsibly
    Validate non-functional findings.
  • 9.10Reporting non-functional findings
    Communicate results clearly.
  • 9.11Knowing the limits of AI here
    Where AI should not be trusted.
  • 9.12A non-functional testing walkthrough
    Practice across these areas.
10Integrating AI into the QA Workflow and GovernanceAdopt AI across the team with standards, review, and measurement.
  • 10.1Mapping AI into your QA process
    Decide where AI adds value.
  • 10.2Tooling and integration choices
    Select tools that fit your stack.
  • 10.3Standards for AI-assisted artifacts
    Set quality bars for AI output.
  • 10.4Reviewing and approving AI output
    Define review and sign-off.
  • 10.5Measuring quality and productivity
    Track outcomes that matter.
  • 10.6Team training and adoption
    Bring the whole team along.
  • 10.7Documentation and knowledge sharing
    Capture and share what works.
  • 10.8Data privacy and compliance in QA
    Stay compliant when using AI.
  • 10.9Avoiding over-reliance on AI
    Keep human judgment central.
  • 10.10Building an AI QA playbook
    Codify your team's approach.
  • 10.11Continuous improvement with AI
    Keep refining your process.
  • 10.12Capstone: an AI-assisted QA plan
    Produce a plan for your own team.