Building Agentic Pipelines with LangChain

Learn to build intelligent retrieval-augmented generation (RAG) systems that answer questions from your own documents. This course teaches you the essential components of RAG pipelines,from document loading and chunking to semantic search and context

Available on May 30th, 2026

Course Curriculum

1. Introduction
2. Learning Objectives
1. Context Is All You Need
2. Agentic AI Implementations
3. Different Types of Models
4. Context-Aware Applications
1. Chat Models
2. Generate Responses with Chat Models
3. Prompt Templates
4. Dynamic Prompt Structures
5. Advance Prompt Templates
6. Complex Prompt Patterns
7. Chat Models & Prompt Templates
1. Example Selectors
2. Semantic Example Retrieval
3. Example Selection
1. Document Loaders
2. Document Ingestion
3. Text Splitters
4. Text Segmentation
5. Document Loaders & Text Splitters
1. Output Parsers
2. Parse Structured Responses
3. Output Parsers
1. Retrievers
2. Query the Knowledge Base
3. Semantic Retrieval
1. Vector Stores
2. Store and Search Vectors
3. Vector Stores
1. Chains
2. Chain Retrieval and Generation
3. Chains and LCEL
1. Graded Quiz
1. Course Summary
1. Document Splitters in LangChain
2. LangGraph Code Assistant
1. LangChain Fundamentals
2. Reference Materials

About this course

Free
39 lessons
7 hours of video content

Building Agentic Pipelines with LangChain

Course Curriculum

Getting Started

Context in Agentic Systems

Context Injection and Prompt Design

Dynamic Example Selection

Document Acquisition and Preprocessing

Output Structuring and Validation

Semantic Retrieval Mechanisms

Vector Indexing and Storage

Pipeline Composition and Orchestration

Self-Assessment

Wrap Up

Supplementary Exercises

Resources

About this course