How should we benchmark an LLM for a specific production use case? I’m trying to understand what a good LLM evaluation process should look like when we’re choosing between multiple models for the same task. Suppose I have 3 candidate models for something like financial document summarization. All three perform reasonably well on general benchmarks. Would you evaluate them using something like: Build a golden dataset from representative production examples Define task-specific quality metrics Run all models against the same dataset and evaluation setup Measure more than just quality: Quality Reliability / consistency Latency Cost Safety Scalability Production fit Analyze individual failure cases rather than relying only on the average score Use the results to decide which model satisfies the original production requirements I’m particularly interested in how people handle LLM-as-a-judge versus deterministic metrics and human evaluation. For example, if Model A has slightly higher quality but Model B is significantly cheaper and more reliable, how do you structure that trade-off? I put together a detailed write-up exploring this approach: [How to Benchmark LLMs for Production — https://medium.com/@hamzashaikhm123/how-to-benchmark-llms-for-production-e375abcd157a/ ] I’d be interested in how others here approach LLM evaluation and what metrics you’ve found useful in practice. submitted by /u/Hamza_1702
Originally posted by u/Hamza_1702 on r/ArtificialInteligence
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