Module 1: Introduction to LangChain

• What is LangChain and why it is the leading LLM framework
• LangChain architecture: chains, agents, memory, and tools
• Setting up the LangChain development environment
• Connecting LangChain to OpenAI, Anthropic, and open-source LLMs

Module 2: Chains and Pipelines

• LangChain Expression Language (LCEL) fundamentals
• Building sequential and parallel chains
• Prompt templates and output parsers
• Combining multiple LLM calls into structured workflows

Module 3: Retrieval-Augmented Generation (RAG)

• RAG architecture and when to use it
• Document loaders and text splitters
• Embedding models and vector store integration
• Building a RAG pipeline with FAISS and Chroma

Module 4: Vector Databases and Semantic Search

• Introduction to embeddings and similarity search
• Integrating Pinecone, Weaviate, and Chroma with LangChain
• Hybrid search: combining keyword and semantic retrieval
• Optimising retrieval for accuracy and performance

Module 5: LangChain Agents and Tools

• What are agents and how they reason with tools
• Built-in LangChain tools: search, calculator, Python REPL
• Building custom tools for domain-specific tasks
• ReAct and OpenAI Function-Calling agents

Module 6: Memory and Conversation Management

• Types of memory in LangChain: buffer, summary, and entity
• Building conversational chatbots with persistent memory
• Managing long conversation context efficiently
• Integrating memory with RAG pipelines

Module 7: LangSmith, Evaluation, and Deployment

• Tracing and debugging LangChain apps with LangSmith
• Evaluating LLM application quality and accuracy
• Deploying LangChain applications with FastAPI and LangServe
• Production best practices: caching, error handling, and monitoring

Module 8: Hands-on Projects and Assessment

• Building a document Q&A system with RAG
• Creating a multi-tool AI agent for a business use case
• End-to-end LLM application deployment project
• Final assessment and course certification

Experience in the Industry Gain expertise from LangChain developers who have built production-grade RAG pipelines, AI agents, and multi-step LLM workflows for enterprise clients.

Backgrounds at the Top Our LangChain trainers have delivered AI application projects for leading technology companies and AI-first product startups globally.

Clear & Effective Teaching LangChain chains, agents, memory, and retrieval components are explained clearly with working code examples for every concept.

Hands-On Learning Focus Students build complete LLM-powered applications including document Q&A systems, AI agents, and RAG pipelines using LangChain and vector databases.

Up-to-Date Knowledge Trainers keep pace with LangChain's rapid release cycle, covering LangGraph, LCEL, and the latest framework updates in every cohort.