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

Name: LangChain for QA: Build RAG, Agentic RAG and AI Chatbots
Rating: 4.8 (9 reviews)

Master LangChain to automate testing workflows. Build RAG, Agentic RAG & Agent Chat UI systems for QA/SDET Professionals

Created bySoftware Testing Mentor & RCV Academy, Manish Verma

Last updated 10/2026

English

English [Auto],

What you'll learn

Design and build LangChain AI agents from scratch that handle test automation workflows
Implement retrieval-augmented generation (RAG) to create intelligent test documentation systems
Create agentic AI solutions that automate bug triage, test analysis, and QA decision-making
Work with LangGraph to orchestrate multi-step workflows for complex QA scenarios
Stream agent outputs for responsive, interactive testing tools
Debug and monitor AI agents using LangSmith for production reliability
Build conversational chatbots that provide intelligent test case and strategy recommendations
Understand system messages, tools, and middleware. These are the core mechanics of AI agents

Course content

14 sections • 109 lectures • 17h 4m total length

Course Overview: The Real Projects You Will Build9:40
A walkthrough of everything you will build in this course, from your first AI agent to a Jira connected RAG system, so you know exactly what you are signing up for.

What Is LangChain and Why QA Teams Are Adopting It9:22
An introduction to LangChain and why it is becoming a core skill for QA and SDET professionals moving into AI augmented testing.
A Quick Tour of LangChain Docs So You Never Get Stuck3:38
Learn how to navigate LangChain's documentation so you can find answers on your own long after this course ends.
Download and Install Python8:34
Step by step Python installation so your environment is set up correctly from the start.
Setting Up VS Code as Your AI Development Environment4:05
Download and install VS Code and prepare it as your primary workspace for this course.
Configuring Python in VS Code Step by Step3:03
Connect Python to VS Code and confirm everything runs correctly before writing any code.
Installing LangChain and Connecting Your First LLM8:21
Install LangChain and the LLM integration packages you will use throughout the course.
Getting Your Anthropic and OpenAI API Keys6:00
Sign up for Anthropic and OpenAI and generate the API keys you need to power your agents.

Build and Run Your First AI Agent in Minutes19:34
Write and run your first working AI agent using LangChain, from scratch.
Extracting Clean Output Text From Your Agent3:35
Learn how to pull the final output text from an agent response instead of dealing with raw objects.
Why System Messages Make or Break Your Agent8:52
Understand how system messages shape agent behavior and how to write ones that actually work.
Giving Your Agent Tools to Take Real Actions11:44
Add tools to your agent so it can go beyond conversation and take real actions.
Initializing and Configuring Your Chat Model8:01
Configure your chat model correctly so it behaves predictably across different use cases.
Streaming Responses for a Real Time Feel12:27
Implement streaming output so your agent responses appear in real time instead of all at once.
Why Your Agent Forgets Everything Without Memory5:30
Understand why agents are stateless by default and why that becomes a problem in real applications.
Implementing Short Term Memory in Your Agent9:24
Add short term memory to your agent so it can maintain context across a conversation.
Mastering the Messages Module in LangChain16:08
A deep dive into LangChain's messages module and how different message types are used.
Teaching Your Agent to Understand Images and More7:18
Learn how to handle multimodal inputs so your agent can process more than just text.
Hands On: Analysing Local Image Files With Your Agent8:38
Feed local image files into your agent and have it analyse and describe them.

Why Free Text Output Breaks Real Applications4:32
Understand why unstructured text output causes problems when you try to use agent responses in real systems.
Forcing Reliable Structured Output With Pydantic11:06
Use Pydantic models to force your agent to return clean, predictable, structured data.
Formatting Structured Output for Downstream Use5:33
Format structured output so it is ready to plug into other parts of your application or test framework.

What Middleware Is and Why Production Agents Need It9:00
An introduction to middleware and the role it plays in making agents safe and production ready.
Automatically Summarizing Long Agent Conversations12:04
Configure summarization middleware so long conversations do not blow past your context window.
Adding Human in the Loop (HITL) Approval to Your Agent10:07
Implement human in the loop middleware so sensitive actions require approval before executing.
Human in the Loop in Action: A Second Real Example5:19
A second hands on example of human in the loop middleware applied to a different scenario.
Protecting Your Budget With Call Limit Middleware11:41
Set model call and tool call limits so a runaway agent cannot rack up unexpected costs.
Never Let Your Agent Go Down: Model Fallback Middleware7:15
Configure model fallback middleware so your agent keeps working even if a primary model fails.
Protecting Sensitive Data With PII Middleware7:32
Implement PII middleware to detect and protect sensitive data flowing through your agent.
Keeping Long Running Agents on Track With Todo Middleware7:34
Use todo middleware to help long running agents stay organized and complete multi step tasks.
Prebuilt Tools and Building Your Own Custom Middleware3:41
Explore LangChain's prebuilt tools and get an introduction to writing your own custom middleware.

What Retrieval Is and Why Your Agent Needs It6:30
Understand what retrieval means in the context of AI agents and why it matters for accuracy.
RAG Architecture Explained11:31
A clear, non technical breakdown of how RAG systems are structured and how the pieces fit together.
Understanding Embedding Models4:58
Learn what embedding models actually do without getting lost in the underlying math.
Mapping Out a Complete Retrieval Pipeline4:46
See the full retrieval pipeline end to end before building it piece by piece.
Loading Real Documents With LangChain Docling9:53
Install LangChain Docling and load real documents into your pipeline.
Configuring Your Hugging Face Token and Symlinks5:38
Set up your Hugging Face token and symlink configuration correctly to avoid common setup errors.
Splitting Text the Right Way for Better Retrieval11:32
Learn how to split documents into chunks that actually improve retrieval quality.
Comparing Text Splitters and When to Use Each3:29
Compare different text splitting strategies and learn when each one makes sense.
Generating Embeddings From Your Documents6:11
Turn your split documents into embeddings ready for storage and search.
Storing Your Documents in a Vector Store7:05
Add your embedded documents to a vector store so they can be searched later.
Running Similarity Search and Building a Retriever12:37
Hands On Project: Build a Complete 2 Step RAG System16:04
Bring everything together into a working 2 step RAG system you built from scratch.

Scaling Your RAG Pipeline to Handle Multiple Files8:34
Extend your pipeline so it can ingest and process multiple files at once instead of just one.
Bringing Excel Data Into Your RAG Pipeline6:52
Learn how to handle Excel files as a data source inside your RAG pipeline.
Handling Mixed File Types in a Real RAG Project7:37
Combine multiple file formats in a single pipeline, closer to what real QA data actually looks like.

Static RAG vs Agentic RAG: What Actually Changes6:42
Understand the difference between static RAG and agentic RAG, and why the shift matters.
Hands On Project: Building Agentic RAG for QA5:53
Build a real agentic RAG project designed specifically around QA use cases.
Turning Your RAG Pipeline Into an Agent Tool10:59
Convert your retrieval pipeline into a tool your agent can call on its own when needed.

Requirements

Basic familiarity with command line and terminal
Prior QA or testing experience is helpful but not mandatory
Python fundamentals (Python tutorials available in course if you are new to Python)

Description

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.

Who this course is for:

SDETs who want to integrate AI into their automation frameworks
QA Leads and Managers exploring how AI can scale QA workflows
QA Engineers/Manual Testers transitioning to AI-powered testing strategies
Test Architects designing next-generation QA systems
Anyone in QA curious about practical AI applications in testing (no ML background needed)

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

What you'll learn

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Course content

Welcome to LangChain for QA: What You Will Build1 lecture • 10min

Get the Course Code and Resources1 lecture • 1min

Hands On Setup: Python, VS Code and LLM API Keys7 lectures • 43min

Build Your First AI Agent: Tools, Memory and Streaming11 lectures • 1hr 51min

Hands On: Structured Output With Pydantic3 lectures • 21min

Production Ready Agents: Middleware for HITL, Fallbacks and PII9 lectures • 1hr 14min

Download Sample Documents Used for Building Agentic RAG1 lecture • 1min

Build a Working RAG Pipeline: Embeddings to Retrieval12 lectures • 1hr 40min

Hands On Project: RAG for PDFs, Excel and Mixed Files3 lectures • 23min

Build a Real Agentic RAG QA Project3 lectures • 24min

Requirements

Description

Who this course is for: