Benchmarks are Useless
Standard academic benchmarks like MMLU or HumanEval are largely useless for production applications. Your enterprise users don't ask multiple-choice questions about high school chemistry. If you deploy a model just because its MMLU score went up, you are flying blind.
LLM-as-a-Judge
We built a robust, two-tier evaluation pipeline. First, we use a larger, more capable frontier model (like GPT-4o) as an automated judge to score the outputs of our smaller, fine-tuned production models.
def llm_as_a_judge(prompt, model_response):
evaluation_prompt = f"""
You are an expert evaluator. Score the following response from 1-5 based on helpfulness, accuracy, and tone.
User Query: {prompt}
Model Response: {model_response}
Output ONLY a JSON object with 'score' and 'reason'.
"""
# Call GPT-4 API to evaluate our internal model
return call_frontier_model(evaluation_prompt)Second, we sample 1% of live traffic and route it to a human annotation team on a custom dashboard. They blindly A/B test the outputs of our old model vs. the new candidate model. We do not push weights to production unless both the LLM Judge and the Human A/B test show statistically significant improvement.