AI / A CONCEPT NOTE
Model Evaluation
measuring LLM output quality systematically
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
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.
02 / FOLLOW THE MECHANISM
How an LLM eval pipeline works
Test dataset
curated set of 100-1000 prompts with expected behaviors — correct answer, style, refusal for harmful queries.
Model under test
generates responses for every prompt in the dataset.
Automated checks
regex/string matches for formatting; BLEU/ROUGE for similarity; code execution to verify generated code.
LLM-as-judge
a second (usually stronger) LLM rates each response on a rubric: accuracy, helpfulness, harmlessness.
Scorecard
aggregates pass rates across categories. A regression alert fires if the score drops below a threshold.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
run an LLM evaluation
python -m llm_judge.evaluate --model my-model --dataset eval_set.jsonl05 / CHECK YOURSELF
Could you explain Model Evaluation to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
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