Module 1: RAG Framework

In this module, you will learn how RAG is used to generate responses for different applications such as chatbots. You’ll then learn about the RAG process, the Dense Passage Retrieval (DPR) context encoder and question encoder with their tokenizers, and the Faiss library developed by Facebook AI Research for searching high-dimensional vectors.

In hands-on labs, you will Use RAG with PyTorch to evaluate content appropriateness and with Hugging Face to retrieve information from the dataset.

Module 2: Prompt Engineering and LangChain

In this module, you will learn about in-context learning and advanced methods of prompt engineering to design and refine the prompts for generating relevant and accurate responses from AI. You’ll then be introduced to the LangChain framework, which is an open-source interface for simplifying the application development process using LLM. You’ll learn about its tools, components, and chat models. The module also includes concepts such as prompt templates, example selectors, and output parsers. You’ll then explore the LangChain document loader and retriever, LangChain chains and agents for building applications.

In hands-on labs, you will enhance LLM applications and develop an agent that uses integrated LLM, LangChain, and RAG technologies for interactive and efficient document retrieval.

M1: RAG Framework

1. RAG

The lecture introduces Retrieval Augmented Generation (RAG), an AI framework designed to enhance the output of large language models (LLMs) by integrating external knowledge sources. Here are the key points:

• Purpose of RAG: It addresses the limitations of pre-trained LLMs, which may struggle with specialized queries due to a lack of domain-specific knowledge.
• RAG Process:
  1. Text Embedding: Converts prompts and knowledge base documents into high-dimensional vectors.
  2. Retrieval: Matches the prompt vectors with knowledge base vectors to find relevant information.
  3. Augmented Query Creation: Combines retrieved information with the original prompt.
  4. Model Generation: The language model generates a response based on the augmented query.
• Encoding Techniques: Uses token embedding and vector averaging to create concise vector representations of prompts and knowledge base content.
• Distance Metrics: Different metrics (like dot product and cosine similarity) are used to identify the most relevant context for generating responses.

This process allows chatbots to provide accurate and contextually relevant answers, especially for domain-specific inquiries.

2. RAG, encoders, and FAISS

In the lecture on RAG Encoders and Faiss, you learned about:

• Retrieval-Augmented Generation (RAG): This approach combines language models with real-time information retrieval, allowing chatbots to provide up-to-date responses without frequent retraining.