Being-Creative-with-AI/C4/Rag-and-its-Uses/English

Title of the Script: RAG and its Uses.

Author: EduPyramids

Keywords: RAG, Retrieval, Generation, Chatbot, AI, EduPyramids, video tutorial.

Visual Cue	Narration
Slide 1 Title Slide	Welcome to this Spoken Tutorial on RAG and its Uses.
Slide 2 Learning Objectives	In this tutorial, we will learn: How an AI model answers questions using additional information What is Retrieval-Augmented Generation (RAG) How RAG helps improve the accuracy of answers
Slide 3 Disclaimer Slide As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.	As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.
Slide 4 System Requirements	To record this tutorial, I am using: Ubuntu 24.04 LTS, and Firefox version 148.0.2 Learners will also need a working internet connection
Slide 5 Prerequisites	To follow this tutorial, Learners should know basic computer and internet usage. No prior AI knowledge is required.
	Let us get started.
Show grocery app order (1 kg onions)	Imagine this: you ordered one kilogram of onions from a grocery app.
Show wrong delivery (tomatoes)	But you receive one kilogram of tomatoes instead.
Show support chat opening	To resolve this, you open the support section of the app. You ask the support chatbot: “I got the wrong item. Can I return it?”
Show chatbot response	The chatbot powered by AI gives you an answer.
Pause on screen (AI thinking?)	But how does it know what to say?
Show AI model trained on large data	The AI model is trained on a large amount of general text data.
Show How AI is helping to understand languages and generating responses as well	This helps it understand language and generate responses.
Highlight limitation	However, this training does not include the app’s return policy. It is unique to each company
Show return policy document	The return policy exists as a separate document on the company’s server.
Show secure storage	This document is stored securely and is not part of the model’s training.
Show search inside documents	When you ask the question, the system first searches the policy document.
Highlight relevant lines	It finds the most relevant information related to your query.
Show info + question sent to AI	This information is sent along with your question to the AI model.
Show final response generation	The AI model then generates an answer based on this information.
Highlight term RAG	This process is called Retrieval-Augmented Generation, or RAG.
Show chatbot without policy	Let us see what happens without a policy document for the chatbot. When a user asks “I got the wrong item in a grocery app. Can I return it?” Without specific information, the chatbot gives a general response. The response may not match the company’s actual return policy.
Show policy document	Now imagine the same question is asked with a policy document.
Display policy points	The policy states that items can be returned within 24 hours. Perishable items cannot be returned. Refunds take 3 to 5 business days.
Ask same question again	We ask the same question again.
Show improved answer	Observe the new response.
Highlight improvement	The answer now clearly follows the given policy.
Compare both responses	The first response is general. The second response is accurate and specific.
Embedding / vector visual	The question is converted into a machine-readable form.
Search visual	The system searches for similar information in the document.
Highlight retrieval	The most relevant part is retrieved.
Send to model	This retrieved content is sent to the AI model.
Final answer generation.	The model generates the final answer using this information.
Slide 6 Key Idea behind RAG RAG helps the AI retrieve relevant information and use it to generate accurate answers. Question → Retrieve → Generate Answer	This is the key idea behind RAG. RAG lets AI fetch relevant data and generate more accurate answers.
	With this, we come to the end of this tutorial.
Slide 7 Summary In this tutorial, we learnt to: How an AI model answers questions using additional information, What is Retrieval-Augmented Generation(RAG), and How RAG helps improve the accuracy of answers.	In this tutorial, we learnt to: How an AI model answers questions using additional information, What is Retrieval-Augmented Generation (RAG), How RAG helps improve the accuracy of answers.
Slide 8 Assignment As an Assignment, Create your own return policy document with at least three rules. Ask a chatbot the same question with and without the document. Compare the responses and observe the difference.	We encourage you to do this assignment.
Slide 9 Acknowledgement Domain Inputs: Bhavani Shankar R and Saisudha Sugavanam Script Writer: Ketki Naina Admin Reviewer: Arthi Varadarajan Quality Reviewer: Sakina Sidhwa Novice Reviewer: Misbah Samir AI Narration: Debosmita Mukherjee AI Graphics: Arvind Pillai Video Editor: Arvind Pillai Web Developer: Ankita Singhal	Thank you for watching.
Slide 10 Acknowledgement This Spoken Tutorial is brought to you by EduPyramids Educational Services Private Limited at SINE, IIT Bombay.

Contributors and Content Editors

Bellatony911, Ketkinaina