Full RAG: A Modern Architecture for Hyperpersonalization

Personalization is crucial in maintaining long-term customer satisfaction and retention for user-centric products like Netflix, Disney, or food delivery apps. AI recommendation engines leverage historical data to provide personalized experiences. Mike Del Balso, CEO of Tecton, discussed using the RAG architecture to improve AI recommendation engine personalization at a recent Unstructured Data Meetup hosted by Zilliz. He highlighted that AI-powered personalization could add $5 trillion in value to global GDP. RAG (Retrieval Augmented Generation) is an effective technique for enhancing the response quality and relevance of large language models (LLMs). It consists of a retriever, which combines an embedding model and a vector database like Milvus or Zilliz Cloud, and a generator, which is the LLM. The RAG pipeline involves transforming all documents into vector embeddings stored in a vector database, converting user queries into vector embeddings, retrieving top candidates from the vector database based on similarity to the query, and generating a coherent response using the query and Top-K candidates. However, traditional RAG systems lack personalized context for users' likes and dislikes. Full-RAG addresses this by adding context in the retrieval pipeline. This involves providing context on candidate locations (e.g., weather, activities) and user preferences (e.g., historical sites, accommodation). Tecton has developed a feature platform to integrate different business data sources for creating personalized contexts at various levels: Base, Batch Context, Batch + Streaming Data Context, and Batch + Streaming data + Real-time Context. RAG is essential in enhancing AI recommendation engines' effectiveness and long-term customer retention. Tecton simplifies building streaming context by providing a Python SDK for coding context definitions and real-time evaluation of data. However, challenges remain, such as managing trade-offs between speed and costs, integrating third-party real-time data sources, and ensuring proper model governance, debugging, and version control.

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