Jupyter Notebooks of Course of LangChain for LLM Application Development by DeepLearning.AI
Learn LangChain directly from the creator of the framework, Harrison Chase. Apply LLMs to your proprietary data to build personal assistants and specialized chatbots. Use agents, chained calls, and memories to expand your use of LLMs.
In this course, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework.
In LangChain for LLM Application Development, you will learn and gain experience with the following topics:
- Models, Prompts and Parsers: calling LLMs, providing prompts, and parsing the response.
- Memories for LLMs: using memories to store conversations and manage limited context space.
- Chains: creating sequences of operations.
- Question Answering over Documents: applying LLMs to your proprietary data and use case requirements.
- Agents: exploring 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.
This one-hour course, instructed by the creator of LangChain, Harrison Chase, as well as Andrew Ng, will vastly expand the possibilities for leveraging powerful language models, allowing you to create incredibly robust applications in a matter of hours.
LangChain for LLM Application Development is a beginner-friendly course. Basic Python knowledge will help you get the most out of this course.
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Harrison Chase
- Instructor
- Co-Founder and CEO at LangChain
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Andrew Ng
- Instructor
- Founder, DeepLearning.AI; Co-founder, Coursera
LangChain for LLM Application Development
Disclaimer: This repository contains educational content from DeepLearning.AI, and I do not claim ownership of this material. The primary purpose of this repository is to serve as a learning resource.