Building LLM Applications using LangChain

• 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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