Summary
LangChain is the state-of-the-art framework for developing Large Language Model (LLM) based applications. It provides a wide range of Lego-like components to streamline the integration of various LLM functionalities into functional pipelines without requiring in-depth expertise in ML.
In this course, students will get an in-depth view of the structure of LangChain and its various components. You will learn how to apply these components to Information Retrieval and the development of chatbots. An overview of the pros and cons of LLMs from OpenAI, HuggingFace, and Anthropic, as well as a primer on Prompt Engineering, will also be provided to empower students to make the best use possible of the capabilities that LangChain puts at their fingertips.
Program
Generative AI
Overview of Generative Models
Comparison of GPT to other LLMs
Text to Image Models
LangChain
LangChain structure
Understanding Chains
Exploring Agents
Using tools to interact with the world
Information Processing
Understanding Text Summarization
Information Extraction Applications
Developing a Question Answering
Chatbots
Information Retrieval and Vectors
Retrievers in LangChain
Implementing a simple Chatbot
Prompt Engineering
Overview of Prompt Engineering Techniques
Comparison of Zero-Shot and Few-Shot Prompting
Understanding Chain of Thought prompts
Developing Tree of Thought prompts