This two-day, 14-hour training course equips software testing and quality assurance professionals with practical skills to integrate artificial intelligence into their QA workflows. Participants will learn how to structure effective prompts and use AI tools to generate and refine test cases, improve test documentation, analyze defects, and support test automation. The course also explores AI tool selection, capabilities, limitations, and the impact of token usage, context windows, response latency, and costs.
Participants will also learn structured approaches to testing AI-enabled applications, chatbots, and virtual assistants. Topics include evaluating response accuracy, consistency, bias, hallucinations, and reliability, as well as testing conversational flows, context retention, adversarial inputs, and regression scenarios. Human oversight and validation are emphasized throughout.
Through a dedicated hands-on workshop, participants will apply these techniques to develop reusable QA deliverables, including test cases, test plans, defect reports, automation examples, and an AI application evaluation report with findings and recommendations.
Duration 2 Days – 14 hrs.
Objectives
- Understand how AI can improve software testing and quality assurance.
- Understand prompt engineering fundamentals, prompt structure, and best practices.
- Use AI assistants such as ChatGPT, Microsoft Copilot, and Claude for relevant QA activities.
- Generate test cases, test documentation, defect reports, and automation scripts using AI.
- Understand how AI can support existing testing tools such as Selenium, Postman, Jenkins, GitLab, JMeter, Playwright, and Automation Studio.
- Understand the fundamentals of AI testing frameworks and structured evaluation techniques.
- Test chatbots and AI-enabled applications for accuracy, consistency, and reliability.
- Understand LLM tokens, context windows, usage limits, and cost considerations.
- Identify practical opportunities for integrating AI into existing QA workflows.
Target Audience
- Software Testers
- QA Analysts
- Test Leads
- Automation Testers
- UAT Coordinators
- QA Managers
- Software Quality Teams
Prerequisites
- Basic knowledge of software testing concepts is recommended.
- Programming knowledge is helpful but not mandatory.
Course Outline
Day 1: AI-Assisted Software Testing (7 hrs.)
Module 1: Prompt Engineering and Introduction to AI in Testing (1.5 hrs.)
- Introduction to generative AI and its applications in software testing.
- Prompt engineering fundamentals and the structure of an effective prompt.
- Key prompt components, including role, context, instructions, constraints, and expected output.
- Prompting best practices and common mistakes.
- Introduction to zero-shot and few-shot prompting.
- Overview of ChatGPT, Microsoft Copilot, and Claude, including their practical applications and limitations.
- How AI supports manual testing and automation testing.
- Benefits, risks, limitations, and the importance of human validation.
Practical Exercise: Use an AI assistant to analyze a sample software requirement, generate test scenarios, and refine the prompt to improve the results.
Module 2: AI for Test Case Design (1.5 hrs.)
- Generating test scenarios and test cases from requirements.
- Creating positive and negative test cases.
- Boundary value analysis and equivalence partitioning.
- Generating test steps, test data, and expected results.
- Identifying missing scenarios and improving test coverage.
- Reviewing and validating AI-generated test cases.
Practical Exercise: Generate test cases for a sample banking application feature, such as login or transaction validation. Review the results and identify missing scenarios.
Module 3: AI for Test Documentation and Defect Analysis (1.5 hrs.)
- Generating test plans, test scripts, UAT scripts, and test summary reports.
- Creating and improving defect reports.
- Analyzing defect descriptions and identifying possible root causes.
- Understanding severity and priority.
- Using AI to summarize QA findings.
- Validating AI-generated recommendations against available evidence.
Practical Exercise: Improve a sample defect report, analyze a simulated test failure, and generate a concise test summary report.
Module 4: AI-Assisted Test Automation and AI Application Testing (2.5 hrs.)
- Generating, reviewing, and debugging automation scripts using AI.
- Practical use of AI with Selenium and Playwright.
- Generating API test scenarios and assertions for Postman.
- Exploring AI-assisted test failure analysis in Jenkins and GitLab workflows.
- Understanding potential applications in JMeter performance testing.
- Discussing AI-assisted workflows for Automation Studio, based on the bank’s existing implementation.
- Introduction to AI testing frameworks and evaluation approaches.
- Testing AI outputs for accuracy, consistency, and reliability.
- Structured testing of chatbots and virtual assistants, including conversational flows, context retention, and fallback responses.
- Identifying hallucinations, bias, and potential security issues.
- Understanding input and output tokens, context windows, token limits, and usage costs.
- Human review and validation of AI-generated outputs.
Practical Exercise: Use AI to generate or review a sample Selenium or Playwright script, examine a Postman test scenario, and analyze a simulated automation failure. Demonstrate a structured checklist for evaluating chatbot responses.
Day 2: Hands-On QA Workshop (7 hrs.)
Module 5: Integrated AI-Assisted QA Workshop
The workshop will apply the concepts from Day 1 to a fictional banking application scenario.
- Requirement Analysis and Prompt Engineering
- Analyze sample requirements.
- Create structured prompts.
- Compare AI-generated outputs using available AI assistants.
- AI-Assisted Test Case Generation
- Generate positive, negative, and boundary test cases.
- Review test coverage and identify missing scenarios.
- AI-Assisted Documentation and Defect Analysis
- Create a test plan and defect reports.
- Analyze simulated failures and prepare a test summary.
- AI-Assisted Automation
- Generate or review a basic Selenium or Playwright script.
- Generate a sample API test for Postman.
- Discuss how the tests could fit into existing CI/CD workflows.
- Chatbot and AI Application Testing
- Design test scenarios for a sample customer-support chatbot.
- Evaluate accuracy, consistency, context retention, and handling of unsupported questions.
- Record findings using a simple evaluation rubric.
- Token Usage and Cost Awareness
- Review token usage where metrics are available.
- Understand context-window limitations, response latency, and cost considerations.
- Discuss factors to consider when selecting an AI model or tool.
- Recommendations for AI Integration in QA
- Identify suitable activities for AI assistance.
- Map practical opportunities to the bank’s existing testing tools.
- Discuss data security, human oversight, and implementation considerations.
- Recommend a phased approach to AI adoption.
- Final Review and Assessment
- Present completed QA deliverables.
- Discuss lessons learned, limitations, and recommendations.
Recommendations for Integrating AI into QA Activities
Trainosys will provide practical recommendations for incorporating AI into existing QA activities, focusing on:
- AI-assisted test-case generation and documentation.
- AI support for automation script creation and debugging.
- Defect analysis and test failure investigation.
- Structured evaluation of chatbots and AI-enabled applications.
- Appropriate use of ChatGPT, Microsoft Copilot, Claude, and relevant evaluation frameworks.
- Opportunities to complement existing tools such as Selenium, Postman, Jenkins, GitLab, JMeter, Playwright, and Automation Studio.
- Data security, output validation, and human oversight.
Recommendations will be based on the tools and access available during the training. The course will demonstrate practical workflows rather than promise end-to-end integrations with the bank’s internal systems.
Expected Training Outcomes
Participants will gain practical experience in generating test cases, preparing QA documentation, analyzing defects, reviewing automation scripts, evaluating AI-enabled applications, and identifying opportunities to incorporate AI into existing testing workflows.
