How Beekeeper optimized user personalization with Amazon Bedrock

This post is cowritten by Mike Koźmiński from Beekeeper.

Large Language Models (LLMs) are evolving rapidly, making it difficult for organizations to select the best model for each specific use case, optimize prompts for quality and cost, adapt to changing model capabilities, and personalize responses for different users.

Choosing the “right” LLM and prompt isn’t a one-time decision—it shifts as models, prices, and requirements change. System prompts are becoming larger (e.g. Anthropic system prompt) and more complex. A lot of mid-sized companies don’t have resources to quickly evaluate and improve them. To address this issue, Beekeeper built an Amazon Bedrock-powered system that continuously evaluates model+prompt candidates, ranks them on a live leaderboard, and routes each request to the current best choice for that use case.

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