LLM Self Evaluation

LLM self-evaluation means using a language model to judge language model output — either the model checking its own answer (“is this correct? rate your confidence”) or a separate LLM-as-judge scoring another model’s response against criteria like faithfulness, relevance, or tone. The judge receives the input, the output, and a rubric, and returns a score or verdict, turning a fuzzy quality question into something you can compute at scale.

This matters because human review doesn’t scale and string matching doesn’t work. You can’t exact-match a generated paragraph against a reference, and you can’t pay people to read a million production responses. LLM judges fill the gap: they’re the engine behind most metrics in DeepEval and Ragas, and they let you grade open-ended outputs in CI and production for pennies. But they inherit model biases — judges favor longer answers, their own model family’s style, and the first option shown — and a model asked to verify its own work often confidently blesses its own mistakes, so self-evaluation is a signal, not ground truth.

In practice, you’ll write judge prompts with an explicit rubric and a constrained output format (a 1–5 score or pass/fail with reasoning), use a strong model to judge a cheaper one, and evaluate one criterion per judge rather than “overall quality.” Mitigate bias by randomizing comparison order and requiring chain-of-thought before the verdict. Most importantly, calibrate the judge: score a sample by hand and measure agreement with human labels before trusting it in your pipeline.