LangChain - Evaluation and Testing
To evaluate a language model's outputs on multiple prompts and compute the average score using Langchain's automated evaluation pipelines, which strategy is most appropriate?
To evaluate a language model's outputs on multiple prompts and compute the average score using Langchain's automated evaluation pipelines, which strategy is most appropriate?
hard📝 Conceptual Q8 of 15
Step-by-Step Solution
Solution:
Step 1: Understand evaluation aggregation
Langchain evaluators typically return per-input scores.Step 2: Aggregate scores properly
Best practice is to collect scores per prompt and compute averages externally or via pipeline aggregation features.Step 3: Analyze options
Use an evaluator that returns scores per prompt and aggregate results externally aligns with this approach. Run separate pipelines for each prompt and manually average scores is inefficient. Configure the pipeline to output only the highest score among prompts ignores averaging. Disable evaluation aggregation and rely on raw outputs disables aggregation.Final Answer:
Use an evaluator that returns scores per prompt and aggregate results externally -> Option AQuick Check:
Aggregate scores after per-prompt evaluation [OK]
Quick Trick: Aggregate scores after evaluation, not before [OK]
Common Mistakes:
MISTAKES- Trying to average scores inside pipeline without support
- Running multiple pipelines unnecessarily
- Ignoring aggregation and using only max score
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