You want to create a custom metric that scores AI answers higher if they contain more keywords from a reference list. Which approach fits best?

hard📝 Application Q15 of 15

LangChain - Evaluation and Testing

ACalculate the difference in length between prediction and reference

BCheck if prediction exactly matches the reference string

CCount how many keywords appear in the prediction, divide by total keywords

DReturn a fixed score regardless of prediction content

Step-by-Step Solution

Solution:

Step 1: Understand the goal of keyword-based scoring
The metric should reward predictions containing more keywords from the reference list.
Step 2: Identify the approach that measures keyword presence proportionally
Counting keywords in prediction and dividing by total keywords gives a score reflecting keyword coverage.
Final Answer:
Count how many keywords appear in the prediction, divide by total keywords -> Option C
Quick Check:
Keyword coverage scoring = Count how many keywords appear in the prediction, divide by total keywords [OK]

Quick Trick: Score by keyword matches divided by total keywords [OK]

Common Mistakes:

MISTAKES

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