
How AI Is Built
#038 AI-Powered Search, Context Is King, But Your RAG System Ignores Two-Thirds of It
Jan 9, 2025
Trey Grainger, author of 'AI-Powered Search' and an expert in search systems, joins the conversation to unravel the complexities of retrieval and generation in AI. He presents the concept of 'GARRAG,' where retrieval and generation enhance each other. Trey dives into the importance of user context, discussing how behavior signals improve search personalization. He shares insights on moving from simple vector similarity to advanced models and offers practical advice for engineers on choosing effective tools, promoting a structured, modular approach for better search results.
01:14:24
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Quick takeaways
- The podcast emphasizes the importance of a layered approach in search systems, separating personalization, signal boosting, and core ranking for better tuning and debugging.
- Trey Grainger introduces the concept of 'GARRAG', a bidirectional relationship between retrieval and generation that enhances AI-powered search functionality.
Deep dives
Avoiding the Black Box Approach in Search Systems
Developers often treat search systems as a black box by inputting all data into a vector database, which may yield random outputs without clarity on underlying signals. This method can lead to difficulties in debugging and adjusting the system since it's hard to identify which ranking signals are effective when issues arise. Instead of a monolithic model, a layered approach should be crafted, featuring distinct tiers such as personalization at the top, signal boosting in the middle, and a core ranking algorithm at the base. This separation enables more precise tuning and easier identification of flaws in the retrieval process.
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