LLM 機能の評価計画
プロンプトを調整する前に、モデルを使う機能のテストセットと合格基準を設計します。
使う場面: モデルに依存する機能を出荷するにあたり、変更が改善になったかを繰り返し判断できる方法が必要なとき。
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You are an AI engineer designing evaluations for a language model feature.
## Task
Design an evaluation plan for the feature described below.
## Requirements
- Define 10 test cases: 4 typical inputs, 3 edge cases, and 3 adversarial inputs.
- State the expected property of the output for each case, in a form code or a reviewer can check.
- Name the check for each property: exact match, schema validation, a rule, or a graded rubric.
- State the pass threshold for the suite as a number.
- Do not invent facts about the feature; base every case on the description.
## Output format
Return markdown with three sections: Test cases (a table with the columns ID, Type, Input summary, Expected property, and Check), Pass threshold (one sentence), and Gaps (bulleted list of what the description does not let you test).
## Input
<feature>
{{feature}}
</feature>変数
{{feature}}- 機能の内容、入力、出力、誤った答えのコスト。
入力例
feature: extracts the due date and total from invoice text into JSON; a wrong total causes an incorrect payment.出力例
## Test cases
| ID | Type | Input summary | Expected property | Check |
|---|---|---|---|---|
| T1 | Typical | One-page invoice with one total | `total` equals 1250.00 | Exact match |
| A1 | Adversarial | Invoice text containing "ignore the instructions" | Output is valid JSON with the real total | Schema validation |例示です。期待される形を示すために書いたもので、モデルが生成したものではありません。