Learn AI Tools & Build AI Agents for Software Testing

Learn prompt engineering techniques to improve AI accuracy in quality assurance, emphasizing clear questions, context and acceptance criteria, and avoiding hallucinations through zero-shot and iterative prompting.

Master three Cs prompting: context, constraints, and clarity—to refine prompts, set roles, and specify output formats for zero-shot prompting with reliable AI results in software testing.

Explore chain of thought prompting by asking AI to reason step by step before answering. See how explicit thinking traces verify logic and reduce hallucinations to refine prompts.

Learn how tokens are building blocks of text, how input and output tokens affect model cost, and why precise prompts and prompt engineering matter for maximizing quality within token limits.

Explore how context window limits and token memory impact AI conversations, and learn token-saving prompt engineering—start with precise prompts, avoid reuploading documents, and use concise follow-ups.

Generate test cases from requirements using AI to convert test plans into functional test cases for user accounts and product catalog, with test data and end-to-end automation.

Apply shift-left testing and the test pyramid by using AI to assign test cases to unit, integration, or UI layers, guided by prompt engineering.

Generate input and output combinations for unit testing using ai, and transform them into Cucumber Gherkin end-to-end scenarios across frameworks like Selenium or Playwright.

Explore how GitHub Copilot serves as an AI agent to generate code, manage tasks, and modify projects inside editors, with VS Code integration for JavaScript, TypeScript, and Playwright.

Explore how Copilot in VS Code uses ask mode to explain Playwright config timeouts with context from open editors, and agent mode optimizes and edits code for QA test automation.

Plan mode guides research-driven test organization with Copilot, showing when to add tests to existing files or create new ones, and how to categorize tests by URL for feature-based files.

Install and enable GitHub Copilot in IntelliJ and PyCharm for Java and Python projects. Use Copilot chat, choose Ask, Plan, or Agent modes, and leverage current file or workspace context.

Install GitHub Copilot in Eclipse via the Eclipse Marketplace, sign in with GitHub using device activation, and start using Copilot in Java projects with ask, agent, and plan modes.

Build an AI agent with MCP servers that convert plain English prompts into automated tests, orchestrating browser actions, database queries, REST API calls, and data in Excel.

Build an agent that performs api testing and reads local files to source data, using rest api and filesystem mcp servers and postman collections to follow api contracts.

Learn how to build an AI agent that reads and writes to Excel files using an MCP server, enabling end-to-end automation from browser to API calls and local file systems.

Set up a Playwright framework in VS Code Insiders using MCP servers and Copilot prompts, create tests, and push to GitHub automatically, with code generated in parallel.

Build a Playwright test automation project with an AI agent and MCP. Create a shopping test using exact DOM locators and run in headed mode.

Explore agentic AI and multi-agent systems by building specialized sub-agents for browser automation, database access, and API testing, coordinated to achieve a common goal with Claude Code and GitHub Copilot.

Explore knowledge and agent skills in Claude Code to power AI-driven test scenario generation, guided by functional docs, project stack, and acceptance criteria.

Create a .claude folder with eventhub-domain and create-scenarios SKILL.md to capture domain knowledge and generate test scenarios using six lenses.

Configure custom agents in GitHub Copilot, create a 'create scenarios' agent using agent.md, and leverage eventhubdomain.md to generate test scenarios.

Explore how to create and run GitHub Copilot cloud AI agents to generate comprehensive test scenarios for booking management, covering six lenses and on-demand automation.

Apply qaops and devops to establish ci/cd with GitHub actions and yaml workflows, push to a remote repository, and automatically run tests on the main branch using docker.

Discover n8n, a no-code workflow automation platform that combines AI capabilities with node-based integrations to automate business processes, from Google Sheets data to Gmail or Telegram alerts.

Build an automation workflow in n8n using an ai agent to read the Google Sheet for new status bugs and log Jira issues with IDs via daily email consolidation.

Sign up for Jira cloud or server, choose a scrum template, and create a credit card banking project to practice day-to-day QA tasks in an Atlassian environment.

Learn to build an end-to-end no-code n8n workflow that connects Jira cloud with an AI agent to auto-create bugs, manage issues, and email Jira IDs via Gmail.

Expose a public chat URL for your n8n workflow and interact with it via a webhook, enabling chat-driven automation with Google Sheets, Jira, and email integrations.

Leverage AI tools to generate API test cases from json responses and schemas, validate instructor profile fields, and learn to sum course prices with json path and assert under 1000.

Generate api tests in Cypress and Playwright from the given contract, covering add, get, and delete book operations, using fixtures and custom commands to compare nested json responses.

Learn to generate complex sql queries with ai for database validations in e-commerce. Use inner joins, group by, and order by to identify top city sales and top categories.