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👉 Welcome to Module 4: Retrieval-Augmented Generation (RAG) and Prompting!

This module is designed to help you understand and implement Retrieval-Augmented Generation (RAG), a cutting-edge technique for augmenting Large Language Model (LLM) responses with relevant external data. You’ll learn how to integrate a vector database like FAISS with an LLM API and craft effective prompts that enhance the model’s ability to deliver accurate and context-rich responses.

Module Overview

Module Overview Objective: Learn to implement RAG to augment LLM responses with relevant context from a vector database and design effective prompts for LLMs.

Lesson Structure

Each lesson within this module includes:

• Introduction Video (Pre-recorded Lecture): Key concepts explained.

• Hands-On Coding Session: Guided walkthrough with downloadable resources.

• Activities & Assignments: Practice exercises to apply your knowledge.

• Quiz: Short quiz to test your understanding.

• Resources: Links to external documentation and courses for deeper learning.

What You´ll Learn in Module 4

Lesson 1: Understanding RAG

• • What is Retrieval-Augmented Generation (RAG)?

  • Importance of RAG in improving LLM responses.

  • Workflow: From embeddings to augmented generation.

Lesson 2: Integrating FAISS with an LLM API

• How to connect a vector database (FAISS) with an LLM API.

• Performing efficient similarity searches for knowledge retrieval.

• Tools and libraries for integrating FAISS with LLMs.

Lesson 3: Designing Effective Prompts

• Principles of effective prompt design for LLMs.

• Crafting prompts that incorporate retrieved data.

• Real-world examples: Summarization, contextual question answering, and recommendation systems.

Course Navigation and Tools

Course Dashboard

• Video lectures and downloadable materials are organized in the dashboard.

• Hands-on labs and coding environments are integrated (e.g., Jupyter, Colab).

Resources Provided

• Sample datasets: Vectorized employee profiles.

• Python scripts: Downloadable starter and solution scripts for each activity.

• API guides: Step-by-step instructions for integrating FAISS with LLM APIs.

• Prompt design templates: Examples and best practices.