Multi-LLM Routing Strategies for Gen AI
summary
Twenty minutes condense experience with hundreds of startups into concrete patterns: use static routing for minimal latency where UI maps to one model, use dynamic routing with an LLM-assisted classifier for fine-grained intent, or use semantic routing via embeddings and a vector DB for domain switches; expect higher latency and extra requests and costs when adding routing layers, mitigate with prompt caching and downstream model choice, and measure routing accuracy and cost tradeoffs empirically while iterating the routing logic.
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chapters (11)
- 0:00Multi-LLM routing intro
- 1:28Why choose multiple language models
- 3:38Multi-LLM use cases
- 5:26Routing challenges
- 6:29Static routing explained
- 8:17Dynamic routing with LLM classification
- 10:00Semantic routing with embeddings
- 11:46Hybrid routing methods
- 14:55Implementation considerations
- 16:19Summary and resources
- 16:41Q&A latency and accuracy
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