Jupyter notebooks cover key concepts and examples, and also include my personal notes and explanations on various topics presented in LangChain for LLM Application Development. This course instructed by the creator of LangChain Harrison Chase as well as Andrew Ng.
In this course you will learn and get experience with the following topics:
- Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the response
- Memories for LLMs: memories to store conversations and manage limited context space
- Chains: creating sequences of operations
- Question Answering over Documents: apply LLMs to your proprietary data and use case requirements
- Agents: explore the powerful emerging development of LLM as reasoning agents.
At the end of the course, you will have a model that can serve as a starting point for your own exploration of diffusion models for your applications.