Decisions from measurement, not instinct: how do you measure LLM success?

The biggest mistake in enterprise adoption of large language models is judging a model by a handful of manual tests and personal impressions. Choosing the right model — and proving the project worked — demands a metric-driven approach. One of werea's core engineering principles: not a single line of code is written before the success criteria and clear test cases are defined.

You have to evaluate a language model's performance in your system against concrete metrics: answer accuracy, faithfulness to the context, latency and token cost. Without them you cannot tell whether an update to the model actually improved the system. No change that cannot be measured and evidenced by data counts as an improvement by engineering standards.