How AI Is Built Podcast - Navigating the Complex Landscape of Modern Search Systems
Stuart Cam
1 November 2024Balancing performance, relevancy, and cost is key to building modern, efficient search systems.
Both Russ and I had the opportunity to speak with Nicolay Gerold on his esteemed How AI is built podcast around Search Architecture, and how we think about some of the constituent components at Search Pioneer.
It was a long conversation, which has been trimmed down to 55 minutes - we hope you enjoy listening as much as we did speaking with Nicolay.
Synopsis
Modern search systems navigate a delicate balance between performance, relevancy, and cost, necessitating thoughtful architectural decisions at each stage. While vector search garners significant attention, hybrid approaches that merge traditional text and vector search capabilities often yield superior outcomes.
Key architectural components include
- ingestion and indexing: deciding between batch versus streaming
- query processing: finding a balance between understanding and speed
- analytics/feedback loops for continuous enhancement.
Often overlooked but critical aspects are the depth of query understanding, systematic relevancy testing to avoid anecdote-based development, and the recognition of data governance as search systems evolve into crucial data hubs for organizations.
Performance optimization is all about strategic trade-offs between index-time and query-time computation, where even small gains of 1-2% can have a substantial impact on mature systems. Effective evaluation requires production data testing, robust infrastructure (golden query sets, A/B testing, interleaving), and vigilance to avoid local maxima— improving one set of queries while unknowingly harming others.
Ultimately, the goal is to strike an optimal balance between corpus size, latency, and cost while ensuring system relevance and manageability. We share some insights and practical advice on balancing these variables, understanding indexing versus query-time processing, and tackling personalization and re-ranking challenges, all while keeping the system's overall health and effectiveness in check.