Hands-On Generative AI Project: MCQ Generator and QA System

Name: Hands-On Generative AI Project: MCQ Generator and QA System
Rating: 4.4 (16 reviews)

Creating Intelligent Assessments and Answer Systems Using Generative AI

Created byMohammed Zeeshan A

Last updated 7/2024

English

English [Auto],

What you'll learn

Multiple-Choice Question (MCQ) Generation Scenario Creation: How to generate realistic scenarios based on given documents or datasets.
Question Answering: Building systems that can accurately answer questions based on the retrieved information.
Integration with AI Models: Combining generative AI models with information retrieval systems to enhance QA capabilities.
Project Execution End-to-End Project Development: Steps to develop, test, and deploy a complete MCQ generator and QA system.

Course content

1 section • 7 lectures • 1h 55m total length

Demo3:03
Watch a live demo of a content processor prototype that generates MCQs from a PDF and uses chunking to power a document-based Q&A system.
Introduction2:57
Build an MCQ generation system and a Q and A system from custom documents using OpenAI and Lang Chain, set up in PyCharm and deployed with Streamlit.
Understanding on OpenAI and Langchain5:25
Explore how OpenAI language models like GPT-3 and GPT-4 power safe, ethical AI and how Langchain streamlines integrating these models into MCQ generation and Q&A systems.
Setting up pycharm6:06
Set up PyCharm for development, create a new QA app project, configure Python 3.10 interpreter, and generate a secure OpenAI API key while understanding pay-as-you-go costs.
Building QA System31:56
Build a QA system in Python by loading documents, chunking data, creating embeddings with OpenAI, storing in a Chroma vector store, and querying with a retrieval QA chain using GPT-3.5-turbo.
Building MCQ Generator32:03
Generate MCQs from PDF documents by chunking text, counting tokens, and querying OpenAI's GPT 3.5 turbo to produce questions with one correct answer and three incorrect answers per chunk.
Building Final Product33:34
Combine the MCQ generator and Q&A system into one Streamlit app with a dropdown UI, using env keys, PDF text extraction, embeddings, and a retrieval QA workflow.

Requirements

Python

Description

Dive into the exciting world of Generative AI with my hands-on project course, "MCQ Generator and QA System: Creating Intelligent Assessments and Answer Systems Using Generative AI." This course will guide you through leveraging OpenAI's powerful language models and LangChain to develop sophisticated applications for education and information retrieval, all on your local system.

What You'll Learn:

OpenAI Language Models: Understand and utilize OpenAI's advanced language models for generating text and answering questions.
LangChain Integration: Learn how to harness the power of LangChain to connect language models with various data sources and workflows.
Scenario-Based MCQ Generation: Create realistic scenarios and formulate multiple-choice questions with varying difficulty levels.
Building a QA System: Develop a robust QA system that retrieves and processes information to provide accurate answers.
Practical Implementation: Use Python to build and deploy your projects locally.
Streamlit UI Development: Create interactive user interfaces with Streamlit to showcase your AI applications.

Course Highlights:

Hands-On Labs: Engage in practical exercises to solidify your understanding and skills.
End-to-End Project: Work on a comprehensive project, from conceptualization to deployment.

By the end of this course, you will have the knowledge and skills to create intelligent MCQ generators and QA systems using the latest advancements in Generative AI, OpenAI, and LangChain, preparing you for a career in AI and machine learning.

Who this course is for:

Beginners in Generative AI