Free text-first course

LangChain for LLM Applications and RAG

A traceable RAG assistant with source citations, structured answers, retrieval evaluation cases, and debugging notes.

  • Lessons in development
  • Free course
  • 42-60 hours
  • 12 modules
  • Download pack included

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Course result

What you will be able to do

A traceable RAG assistant with source citations, structured answers, retrieval evaluation cases, and debugging notes.

Work clearly

Use a repeatable workflow instead of disconnected tips.

Review carefully

Check assumptions, evidence, output quality, and limitations before using results.

Finish with evidence

Create a capstone artifact that can be inspected, improved, and reviewed.

Course details

What you need before starting

For

Learners who want to use LangChain to build maintainable LLM workflows, RAG chains, simple agents, and traceable components.

Prerequisite

Python Foundations, Generative AI Application Development with Python, and Introduction to RAG are recommended.

Format

Static lessons, activities, knowledge checks, learner templates, and a downloadable text-first pack.

Syllabus

12-module course sequence

The sequence follows the approved detailed syllabus and is implemented as a complete text-first shell.

Module 01

LangChain's Role in the LLM App Stack

  • Learn how LangChain’s Role in the LLM App Stack fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 02

Models, Messages, Prompts, and Provider Configuration

  • Learn how Models, Messages, Prompts, and Provider Configuration fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 03

Structured Output and Parsers

  • Learn how Structured Output and Parsers fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 04

Document Loading, Splitting, and Embeddings

  • Learn how Document Loading, Splitting, and Embeddings fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 05

Vector Stores and Retrievers

  • Learn how Vector Stores and Retrievers fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 06

Retrieval Chains with Grounded Responses

  • Learn how Retrieval Chains with Grounded Responses fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 07

Tools and Simple Agents

  • Learn how Tools and Simple Agents fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 08

Memory and Session State Boundaries

  • Learn how Memory and Session State Boundaries fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 09

Streaming and Callbacks

  • Learn how Streaming and Callbacks fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 10

Tracing and Debugging with LangSmith

  • Learn how Tracing and Debugging with LangSmith fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 11

Testing and Evaluating LangChain Components

  • Learn how Testing and Evaluating LangChain Components fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.
Module 12

Capstone: Traceable RAG Assistant

  • Learn how Capstone: Traceable RAG Assistant fits into LangChain LLM applications and RAG instead of treating it as an isolated topic.
  • Practice by build or inspect a small LangChain component using the module idea, then compare the result with the module review checklist.
  • Produce a traceable component note with inputs, outputs, retrieval behavior, and tests before moving ahead in LangChain for LLM Applications and RAG.

Course status: outline published, lessons in development

The full syllabus, module structure, activities, and download pack are published and free to use now. Detailed lesson writing is still in progress, so lesson pages currently give the shape of each topic rather than the final teaching depth. Per-module assessments, worked examples, instructional diagrams, and external specialist review are planned next.

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