The article discusses insights from Denys Linkov's presentation on building micro metrics for evaluating LLM systems, emphasizing the specific challenges LLMs face in real-world applications. The need for careful observability and metrics that align with business objectives is paramount. A notable scenario demonstrates the importance of precise metric tracking—miscommunication arose when a model unexpectedly switched languages during a user interaction. Linkov advocates for a methodical approach to developing metrics, suggesting a crawl-walk-run strategy that simplifies the process of system improvement in AI settings.
Each problem in the AI space has unique challenges. Once you've been serving production traffic, you'll find edge cases and scenarios you want to measure.
Consider models as systems: LLMs are part of broader systems. Their performance and reliability require careful observability, guardrails, and alignment with user and business objectives.
Build metrics that alert of user issues and make sure you have a cleanup process to phase out outdated metrics.
Focus on business direction. Build metrics that align with your current goals and the lessons learned along the way.
Don't overcomplicate it. Adopt a crawl, walk, run methodology to incrementally develop metrics, infrastructure, and system maturity.
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