AI in Testing
Transform your testers into an AI powered testing force.
What changes when your
testing team goes AI-Powered.
43% of organizations are still experimenting with GenAI in QA, while only 15% have scaled it enterprise-wide. The opportunity isn’t adopting AI. It’s knowing how to make it work at scale.
What your team
walks away with.
Everything your team
will actually cover.
01 Agentic AI Foundations & Environment Setup
02 Advanced Prompt Engineering for Testers
03 Playwright MCP: AI-Powered Test Automation
04 Building Your First QA Agent
05 LangChain for QA Automation
06 LangGraph: Orchestrating Multi-Step QA Workflows
07 RAG for Test Automation (Retrieval-Augmented Generation)
08 Agent Memory & Context Persistence
09 Tool & Test-Management Integration
10 Multi-Agent Test Automation Systems
Hear from teams
we've trained.
“We received a solid foundation covering generative AI and RAG. The trainer never cut content to stick to the scheduled time and went beyond the allotted hours to cover everything we asked for.”
“The team was able to arrive at the same level of understanding. We're looking forward to launching agents and agentic workflows in the next few sprints, and we would look forward to collaborating again for another engagement.”
Sample trainer
profiles.
Seventeen years spanning data, cloud, automation and DevSecOps, now leading applied AI engineering at a global financial-data enterprise. Published author on generative AI.
Runs cross-functional GenAI programs for QA, dev, DevOps, data and leadership teams across four countries. Internationally certified AI trainer, Singapore-accredited.
Twelve years of hands-on automation across fintech and payments-scale platforms. Speaks and organizes meetups across the European QA community, with 863 learner reviews to date.
Profiles are anonymized at this stage. Full profiles are shared once we scope your program.
REQUEST TRAINER PROFILESChoose how
your team learns.
What makes
us different.
THE TEST TRIBEFrequently asked
questions.
What's the difference between AI, Generative AI and Agentic AI?
AI is the umbrella term for any system that performs tasks requiring human-like intelligence. Generative AI is a subset that creates new content (text, code, images) from a prompt. Agentic AI goes further: it uses generative AI as one component inside a system that can reason, plan, use tools, and act autonomously toward a goal, such as an agent that writes, executes and repairs its own tests.
What is Playwright MCP?
Playwright MCP connects Playwright’s browser automation to the Model Context Protocol (MCP), letting an AI agent see and act on a live browser context, reading the DOM, generating locators, and writing or self-healing UI tests instead of a human scripting every step manually.
What tools does this AI-in-testing training cover?
The program covers Claude and ChatGPT for prompting, Playwright MCP for AI-driven UI automation, and LangChain, LangGraph, RAG and multi-agent architecture for building custom QA agents, plus integrating those agents with test management tools like Jira and TestRail.
Is this training available for corporate/enterprise teams outside India?
Yes. The program is delivered live for enterprise QA and SDET teams globally, with batch timings offered across IST, EST and CET so distributed teams can join together.
Who is this training designed for?
It is designed for QA engineers, testers, automation engineers, quality engineers, test leads, and engineering professionals looking to integrate AI into their testing workflows.
What are the prerequisites for AI in Testing training?
Basic knowledge of manual or automation testing is preferred but not essential. Participants should be familiar with basic software testing concepts, but prior AI experience is not required.
Will the training include hands-on projects?
Yes. The program includes practical labs covering AI-generated test cases, automated test-step creation, prompt optimization, autonomous test generation, self-healing selectors, RAG-powered agents, and multi-agent log analysis.
How is AI in Testing different from traditional test automation training?
Traditional test automation focuses on automating predefined testing workflows. AI in Testing goes further by enabling teams to build intelligent systems that can generate tests, understand context, interact with tools, adapt to changes, and support testing decisions.
What will I be able to do after completing the training?
You’ll be able to identify practical AI use cases in testing, build AI-powered testing workflows, work with AI agents and MCP, automate repetitive testing activities, and apply AI to improve testing efficiency and coverage.
Can this training be customized for our organization?
Yes. The training can be adapted to your organization’s testing workflows, technology stack, team requirements, and specific AI adoption goals.
Let's
talk
Tell us where your team is today. We'll map the shortest path to an AI-in Testing, with the right curriculum, trainers and outcomes committed before we start.