Have you ever wished your interactions with AI were more personalized and context-aware? That's exactly what the AI Context Generation Interviews project sets out to achieve. Let me walk you through this fascinating demonstration of how we can make AI interactions more meaningful and personalized.

What's This All About?

At its core, this project showcases an innovative approach to enhancing AI interactions through what we call "agentic workflow-based context extraction." In simpler terms, it's a smart system that actively learns about you through natural conversation, then transforms those insights into structured data that can make AI interactions more personalized.

How Does It Work?

The magic happens through a straightforward workflow:

Choose Your Focus: Start by selecting an area you'd like to discuss
Have a Conversation: Engage in a natural interview with the AI agent
Behind the Scenes: The system automatically extracts and processes relevant context from your conversation
Ready to Use: Get your personalized context data in a format ready for use with LLMs

The Cool Parts

What makes this project particularly interesting is how it:

Stays Proactive: Instead of passively collecting data, the system actively generates meaningful context through targeted questions
Keeps It Natural: The interview format feels like a regular conversation, not a formal data collection process
Makes Data Useful: All that conversational data gets transformed into a format that's perfect for RAG pipelines and vector databases
Builds Over Time: You can have multiple interviews, gradually building a richer context pool for more personalized AI interactions

Screenshots

Technical Implementation

The system is built using Streamlit for the frontend and implements a sophisticated workflow that:

Automatically extracts context from user interactions
Generates contextual metadata proactively
Integrates smoothly with LLM inference processes
Enables progressive enhancement of personalization

Why This Matters

In the world of AI, context is king. The better an AI system understands you, the more valuable its interactions become. This project demonstrates a practical approach to building that understanding through natural conversation, making it easier than ever to create truly personalized AI experiences.

Try It Yourself

Want to see it in action? Head over to the Hugging Face Space and give it a try. The entire project is open source and available on GitHub, so you can also dive into the code and see how it all works under the hood.

Attribution

This project represents a collaboration between Daniel Rosehill and Claude (Anthropic), demonstrating how human creativity and AI capabilities can come together to create innovative solutions for enhancing AI interactions.

Using AI Agent Interviews To Develop Context Data (Demo)

Enhancing LLM Interactions Through AI-Driven Context Generation