Beyond vibes: How to properly select the right LLM for the right task

Choosing the right large language model (LLM) for your use case is becoming both increasingly challenging and essential. Many teams rely on one-time (ad hoc) evaluations based on limited samples from trending models, essentially judging quality on “vibes” alone.

This approach involves experimenting with a model’s responses and forming subjective opinions about its performance. However, relying on these informal tests of model output is risky and unscalable, often misses subtle errors, overlooks unsafe behavior, and provides no clear criteria for improvement.

A more holistic approach entails evaluating the model based on metrics around qualitative and quantitative aspects, such as quality of response, cost, and performance. This also requires the evaluation system to compare models based on these predefined metrics and give a comprehensive output comparing models across all these areas. However, these evaluations don’t scale effectively enough to help organizations take full advantage of the model choices available ...