If you’re shipping AI agents without offline validation, your users are the ones doing the testing. Get the practical framework for evaluating production-grade AI agents before they hit production. Get the guide to learn how to: Build annotated test datasets that cover core use cases, edge cases, and adversarial inputs Design deterministic and LLM-as-a-judge evaluators that reflect real business impact Trace multi-agent workflows end-to-end during experimentation to catch failures before users do Prevent model drift by keeping your offline test environment aligned with production.

Large Language Models (LLMs) are also software systems just like any other software system we may have encountered. But we cannot test an LLM the same way as an ordinary software system. For example, a normal function can receive two numbers and always return the same number as the total. However, when we ask the same question twice to an LLM, it will most likely produce two differently worded answers. Both may be acceptable. But it makes evaluation tricky. To evaluate an LLM, we have to measure whether the application continues to behave properly across many situations.

“LLM-as-a-Judge” is one part of this evaluation process. It involves using one language model to assess the output generated by another language model. But a judge model isn’t enough on its own. A healthy LLM evaluation system combines several ingredients such as conventional software tests, carefully curated examples, automated checks, model-based judging, human review, and production monitoring.

When can we call an LLM healthy? An LLM is deemed healthy if it consistently generates useful results while remaining within acceptable limits for accuracy, safety, speed, reliability, and cost. For example, consider a customer-support assistant. We cannot say it is healthy by a single question such as “Did it return the correct output?” We need to consider several different questions: Did it understand what the customer was asking? Was the answer factually correct based on the company documentation? Did it answer the entire question? Did it follow the required tone and format? Did it avoid inventing policies that do not exist? Did it refuse requests that it should not answer? Did it respond within an acceptable amount of time? Did the request cost an acceptable amount to process?