TOPIC AI
Model Evaluation measuring LLM output quality systematically
IN 10 SECONDS
Evaluating an LLM is harder than evaluating a traditional model because there's no single 'correct' answer. Evaluation frameworks use a mix of automated metrics (BLEU, ROUGE for summarization, exact match for code) and LLM-as-a-judge (a stronger model rates the output). A good eval set covers accuracy, safety, formatting, and edge cases.
GOTCHA LLM-as-judge inherits the judging model's biases — it may prefer longer answers, specific phrasing, or favor its own style. Use multiple judges, shuffle answer order, and periodically validate judge decisions with human reviewers.
HOW AN LLM EVAL PIPELINE WORKS
01 Test dataset curated set of 100-1000 prompts with expected behaviors — correct answer, style, refusal for harmful queries.
02 Model under test generates responses for every prompt in the dataset.
03 Automated checks regex/string matches for formatting; BLEU/ROUGE for similarity; code execution to verify generated code.
04 LLM-as-judge a second (usually stronger) LLM rates each response on a rubric: accuracy, helpfulness, harmlessness.
05 Scorecard aggregates pass rates across categories. A regression alert fires if the score drops below a threshold.
POKE IT YOURSELF
python -m llm_judge.evaluate --model my-model --dataset eval_set.jsonl — run an LLM evaluation
Drill this topic →
~75 sec read