"Many Agent systems today remain surprisingly static. Their behavior is tightly constrained by brittle, hand-written prompts produced through trial-and-error, and sometimes vibes."
"The real challenges become LLM Observability, context management, feedback collection, and automated optimization. In other words, how do we design systems that can measure their own behavior and then improve it over time?"
"Measuring the performance of an LLM is very difficult due to the nondeterministic nature of the output, the unstructured format of the 'correct' answer, and the arbitrary nature of the problems many LLM applications are built to solve."
"LLM evals are just metrics. It can be a heuristic metric or an LLM-as-a Judge, systematic methods for measuring how we evaluate performance."
AI agents have the potential for significant efficiency gains, but many remain static due to reliance on brittle prompts. As LLMs evolve, challenges shift towards observability, context management, and automated optimization. The focus is on designing systems that can self-measure and improve. LLM evals are introduced as metrics to assess performance, addressing issues like nondeterminism and subtle reasoning errors. Effective evaluation is crucial for enhancing agent capabilities and ensuring consistent performance across various scenarios.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]