When preparing for highly competitive exams like UPSC, aspirants often struggle to find specific Previous Years' Questions (PYQs) based on topics or keywords. The traditional method of searching through PDFs or books is time-consuming and inefficient. Enter tackles this by leveraging AI-driven automation, React, Python, and vector search to create a fast and intuitive platform.
Tech Stack Overview
Frontend: React
The frontend is built with React, offering a responsive and interactive user experience.
Features like keyword search and filters ensure that users can navigate through thousands of questions effortlessly.
SEO-friendly practices like proper meta tags and dynamic rendering ensure better discoverability.
Backend: Python
The backend uses Flask, a lightweight Python framework, to handle requests and integrate AI services.
Python's versatility made it ideal for working with NLP models and vector-based search.
AI-Powered Question Tagging
ChatGPT: OpenAI's GPT model was employed to automate the tagging of questions based on keywords and topics.
By processing questions in batches, GPT assigns macro and micro-level tags like polity, article 15, or revolt of 1857. While not perfect, it significantly reduced manual effort.
Vector Search for Relevance
Vector Search: To enhance search precision, questions are embedded into vectors using OpenAI's embeddings.
Pinecone (or similar vector databases) ensures fast and accurate retrieval of questions, even for loosely related keywords.
This allows users to search for topics semantically, such as retrieving questions about fundamental rights by simply typing "rights in the constitution."
Hosting and Deployment
The platform is hosted on Vercel for frontend and AWS for backend APIs.
CI/CD pipelines streamline updates, ensuring seamless user experiences.
How It Works
Data Collection:
PYQs are collected from public repositories and reliable sources.
Questions are preprocessed to remove duplicates and irrelevant data.
Tagging with ChatGPT:
The GPT model analyzes each question and suggests appropriate tags.
Tags are then validated and stored in a database for efficient retrieval.
Search Implementation:
Users type a keyword (e.g., river systems), and the system matches the query against the tagged database using vector search.
Results are displayed instantly with related tags to encourage further exploration.
User Experience:
The React-based frontend provides real-time search and a clean interface, optimized for both desktop and mobile.
Challenges and Learnings
Automating Tagging: While ChatGPT performed well, edge cases like ambiguous or multi-topic questions required manual intervention.
Optimizing Search: Fine-tuning vector embeddings and query parameters was essential to improve accuracy and relevance.
Scalability: Ensuring the platform could handle large datasets and thousands of queries without performance issues was a top priority.
Why This Matters
is changing the game for UPSC preparation. By blending AI with intuitive design, it provides a smart, fast, and effective way to access topic-wise PYQs.
Try it out, and let me know your feedback! Your insights will help make the platform even better. 😊
Would you like to know more about the tech behind Turtle & Rabbit? Let’s discuss in the comments! 🚀
This concise, tech-focused article is optimized for dev.to while integrating SEO keywords like UPSC preparation, vector search, React, Python, and AI. Let me know if you'd like further tweaks!
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