Mike Del Balso - Full RAG: A Modern Architecture for Hyperpersonalization | PyData Vermont 2024

AI-driven personalization is expected to unlock $5 trillion in GDP by 2030 through step-function improvements, not incremental changes

Effective AI personalization requires rich context about users, which includes:

• Profile data from data warehouses
• Historical behavior and preferences
• Real-time streaming data from current sessions
• External API data (weather, events, etc.)

Building context systems is a major data engineering challenge:

• Requires complex data pipelines
• Real-time data retrieval and joining
• Large engineering teams at big tech companies
• Expensive and time-consuming to implement

Four levels of context personalization:

• Zero context (generic recommendations)
• Batch context (historical data)
• Real-time context (current user state)
• Memory/feedback (iterative learning)

Context platforms are emerging as a new layer in the AI stack:

• Handle data retrieval and assembly
• Manage real-time and historical data
• Enable personalization without massive engineering teams
• Keep implementation complexity manageable as systems scale

Success factors for personalized AI systems:

• Must feel like recommendations from a knowledgeable friend
• Requires both historical and real-time data integration
• Need explanations for recommendations
• Should allow user feedback and iteration
• Must be cost-effective to implement and maintain