This course provides a comprehensive guide to building applications with Large Language Models using the LangChain framework. It covers the essential components for creating sophisticated, data-aware, and agentic systems. You will learn to structure prompts, chain models together, connect to external data sources for Retrieval Augmented Generation (RAG), and build autonomous agents that can reason and act. The curriculum moves from foundational concepts like models and prompts to constructing complex, multi-step chains and agents, preparing you to develop production-ready LLM applications.
Prerequisites Intermediate Python experience
Level:
LangChain Fundamentals
Understand the core architecture and components of the LangChain framework for LLM application development.
Prompt Engineering
Construct dynamic and effective prompt templates to guide LLM outputs for various tasks.
Chains and Sequential Processing
Build and manage multi-step workflows by linking LLMs and other components into sequential chains.
Retrieval Augmented Generation (RAG)
Integrate LLMs with external data sources using document loaders, vector stores, and retrievers to build Q&A systems.
Conversational Memory
Implement stateful applications by adding different types of memory to manage conversation history.
Autonomous Agents
Develop agents that can use tools to interact with their environment, make decisions, and complete tasks.
Application Monitoring
Use LangSmith to trace, debug, and monitor the performance of your LangChain applications.
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