LangChain for QA: Build RAG, Agentic RAG and AI Chatbots

About this course

LangChain is transforming how QA professionals approach test automation and quality assurance. If you're tired of purely manual testing workflows and want to leverage AI to make smarter decisions, faster, this course is built for you.

What makes this different? Unlike generic AI courses, we focus on practical LangChain implementation for QA professionals. You'll learn to build production-ready AI agents that handle test analysis, automate bug triage, process test data with retrieval-augmented generation (RAG), and create intelligent chatbots that answer testing questions. All without needing a machine learning background.

Throughout this hands-on course, you'll progress from LangChain fundamentals to building three real-world solutions: a RAG system that answers questions from your test documentation, a conversational chatbot for test case recommendations, and an agentic AI workflow specifically designed for QA automation. You'll understand system messages that guide AI behavior, implement memory to maintain context across conversations, stream outputs for responsive interfaces, and handle multimodal inputs like screenshots and logs from your test environments.

We cover everything step-by-step: setting up Python and VS Code, integrating APIs from Anthropic and OpenAI, working with LangGraph for complex workflows, and using LangSmith for debugging and monitoring your agents. You'll also explore middleware, memory management, and tools. These are the building blocks of effective AI agents.

By the end, you'll have a complete toolkit to bring AI into your QA strategy. You'll automate repetitive testing decisions, build knowledge systems that learn from your test history, and create AI-powered workflows that scale your QA efforts without adding headcount.

Whether you're an SDET, QA engineer, or test automation specialist, you'll gain hands-on skills to stay ahead in an AI-augmented testing landscape.

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