Have you ever wished your AI assistant was just a little more you?
This blog post explores an architecture for generating personalized context data for AI systems, moving beyond passive data collection to a proactive, interview-driven approach. Imagine an AI that truly understands your needs and preferences, offering assistance that’s both accurate and relevant. That's the power of personalized context.
The Problem with Generic AI
Today's large language models (LLMs) are impressive, but their knowledge is based on broad training data. This generic approach lacks the personal touch. While we rightly protect sensitive information, even small amounts of personalized context can dramatically enhance an LLM's ability to provide useful insights.
Retrieval Augmented Generation (RAG) has emerged as a key technique. RAG uses embedding models to convert files into numerical representations, stored in vector databases optimized for LLM access. Imagine uploading your resume to a vector database connected to a career advice AI. Suddenly, the advice becomes incredibly personalized.
This process can be dynamic. Instead of manual uploads, a constantly updated data store (like your Google Drive) can feed a RAG pipeline. This is exciting for business workflows. Imagine a support team accessing a model connected to the company's internal knowledge base. The possibilities are endless.
Diagram
Proactive Vs. Passive Context Generation Approaches
A More Proactive Approach: The AI Interview
Current RAG pipelines and memory stores passively create context. They extract and convert existing data, which can be slow and doesn't always capture the nuances of individual needs.
My proposed system takes a proactive approach.
It deliberately generates context data through a structured "interview" process, using the following components:
1. The "Interviewer" AI: This AI assistant, created using existing APIs or system prompts, acts as an inquisitive interviewer. You can even create multiple interviewer bots, each specializing in a different area of your life.
Here's an example system prompt to get you started:
Your purpose is to interview the user, asking a wide range of questions to gather substantial data about their life. This data will be used to create a personalized context store for improving LLM-based assistants.
Begin by asking if the user wants to focus on a specific domain (e.g., professional life, health). Tailor your questions accordingly (e.g., career objectives, medical history).
[... further prompt instructions as in the original text ...]
2. The Vector Store Pipeline: The interviewer AI can directly write data into the context pipeline, or the user can manually copy and upload the information.
3. The Personal Agent: This agent accesses the personalized context data store, providing tailored guidance. The more information gathered, the better the assistance.
4. The Data Classification Agent (Optional): This agent can categorize the data, separating sensitive information from less sensitive context that could be shared with other services. Imagine this as your personal data footprint, automating form filling and other tedious tasks.
Context Aggregation: How Deliberate And Passive Context Generation Could “Coexist” In Integrated RAG Pipelines
Empowering Users with Data Control
This architecture prioritizes data sovereignty. You control your context data. You choose what to share, and what to keep private.
Potential Use Cases: From Personal to Professional
Imagine using a speech-to-text interface to conduct these interviews, making the process even more engaging. Or think about a sales team providing focused information about their quarterly targets, jumpstarting a powerful, personalized AI tool.
These systems can also coexist, integrating data from multiple sources – chat history, deliberate context data, and information from internal tools – to create a comprehensive context repository.
The Future of Personalized AI
This interview-driven approach represents a powerful step towards truly personalized AI. It's about empowering users to shape their AI experience, creating assistants that are not just smart, but truly understand them