Generative AI Chatbots for QA Automation (2026)

About this course

This course is designed for QA Engineers, Automation Engineers, Developers, and SDETs who want to build production-ready AI chatbots that support testing, validation, analysis, and automation workflows.

You will learn how to design and implement LLM-powered chatbots using Python, LangChain, Streamlit, and LangSmith, with support for both cloud-based and local models. Rather than building toy demos, this course focuses on real-world chatbot architectures that include memory, observability, persistent storage, and modular design.

The course covers the complete lifecycle of a chatbot:

• Designing scalable chatbot architecture
• Building interactive chat UIs with Streamlit
• Managing conversation memory and chat history
• Switching between LLM providers dynamically
• Observing, debugging, and tracing chatbot behavior using LangSmithImplementing Retrieval-Augmented Generation (RAG) for document-based QA
• You will also learn how to store and retrieve conversation history using a SQL database and how to deploy chatbots locally or in cloud environments.

All concepts are taught through hands-on, step-by-step implementations, following production-style engineering practices rather than experimental demos.

By the end of this course, you will be able to design, build, and deploy AI chatbots that can be used as QA assistants, validation tools, and internal automation helpers.

What you will be able to do after this course

Design modular and scalable AI chatbot architectures

Build interactive chat UIs using Streamlit

Implement chatbot memory and persistent conversation history

Switch between cloud and local LLM providers

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