Project Overview
Type: Personal Project
Tech Stack: Python, Streamlit, OpenAI GPT
Role: End-to-end builder (ideation, development, deployment)
Timeline: 1-week prototype
Summary
AI FAQ Builder is a web application that allows users to upload PDF documents and receive AI-generated FAQs based on the document’s content. It streamlines the process of knowledge extraction using OpenAI's GPT models and presents the results in a user-friendly format.
Problem Statement:
Organizations often work with large, text-heavy documents such as employee handbooks, contracts, technical manuals, or government policies. When new users or stakeholders need quick answers, they either comb through lengthy documents or repeatedly ask the same questions—both time-consuming and inefficient.
Solution:
AI FAQ Builder addresses this by enabling users to:
Upload any PDF document
Automatically extract frequently asked questions from the content using GPT
Download the resulting FAQs for use in customer support, internal training, or documentation
Built using Streamlit, the tool offers a simple and accessible interface without requiring technical expertise.
How It Works:
1. Upload a PDF document via the web interface
2. Extract content from the file using Python’s PDF libraries
3. Generate concise FAQs using OpenAI’s GPT model
4. Download the final output as a clean text file or pdf file.
The system is powered by a .env file configuration to securely handle API keys.
Tools and Techniques
OpenAI GPT – content analysis and FAQ generation
Streamlit – interactive web app interface
Python – backend logic and PDF processing
Environment Configuration – secure API key handling
Key Learnings
Designing workflows that connect AI APIs with user interfaces
Handling document parsing and data cleaning for NLP tasks
Simplifying AI outputs for practical, non-technical users
Rapid prototyping using no/low-code deployment tools
Real Life Use Cases
HR teams generating FAQs from employee handbooks
Legal departments summarizing contracts
Educators creating questions from reading materials
Startups creating help center content from technical documents
Government offices and agencies automating responses to citizen queries
Key Outcomes / Results:
Designing workflows that connect AI APIs with user interfaces
Handling document parsing and data cleaning for NLP tasks
Simplifying AI outputs for practical, non-technical users
Rapid prototyping using no/low-code deployment tools