Evaluating LLM Solutions
How to assess whether an AI solution actually solves the client's problem
What it is
Evaluating an LLM-based solution requires thinking across several dimensions: Task fit (does the task actually benefit from an LLM, or is a simpler rule-based system more appropriate? Reliability requirements) can you tolerate occasional wrong answers? Cost model (what's the per-query cost at your expected volume? Latency requirements) can users wait 30 seconds for a reasoning model?
A structured evaluation process: define success metrics before testing, build an eval set of representative inputs, test multiple models on the eval set, and measure against your metrics rather than vibes.
Common failure modes: choosing a model based on benchmark rankings rather than your specific task, neglecting edge cases, and not testing at realistic input distributions.