Renzo Caputo — designing evals for LLM agents

Designing effective evaluations for LLM agents is a complex but crucial task. One clear insight from my experience is that we must prioritize context-aware assessments over traditional accuracy metrics. While metrics like precision and recall are valuable, they often fail to capture the nuanced understanding required in real-world applications. When creating evaluation frameworks, consider scenarios that reflect the agent's intended use case. Incorporate qualitative feedback to assess not just the correctness of responses, but also their relevance and appropriateness. This dual approach can bridge the gap between engineering capabilities and market needs, ensuring that the LLMs we deploy are not just technically sound, but also contextually intelligent. As we refine these evaluations, we pave the way for more reliable and effective AI-driven solutions.