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Generative AI Chatbots for QA Automation (2026)
Ship a production-shaped QA chatbot with LangChain, LangSmith observability and a Streamlit UI.
- Duration
- 2 hours
- Lectures
- 16
- Level
- All Levels
- Learners
- Active learners
2.9 on Udemy (3 ratings)
- Python
- LangChain
- LangSmith
- Streamlit
- LLMs
About this course
Hands-on path to design and deploy production-ready QA chatbots using modern LLM tooling.
What you'll learn
- Production QA chatbots with LangChain
- Observability with LangSmith
- Streamlit UIs for QA teams
What you'll build
A QA-focused chatbot built with LangChain
LangSmith tracing and observability for every run
A Streamlit interface your QA team can actually use
Project architecture
The pipeline you assemble across the course:
- User question
- Streamlit UI
- LangChain chain
- LLM
- LangSmith tracing
- Answer + observability
Curriculum overview
16 lectures · 2 hours of on-demand video.
01Chatbot foundations
- LLM basics for QA use cases
- Designing the conversation scope
02Building with LangChain
- Chains, prompts and memory
- Wiring in your own content
03Observability
- LangSmith tracing
- Debugging bad answers
04Delivery
- Streamlit UI for QA teams
- Deployment considerations
FAQ
Do I need an ML background?
No — this is applied engineering with LLM APIs, not model training.
Why LangSmith?
Because a chatbot you cannot trace is a chatbot you cannot test. Observability is treated as a first-class QA concern.
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Ready to start Generative AI Chatbots for QA Automation (2026)?
Lifetime access, 16 lectures, and a project you can put on your CV.