Evaluation Metric: Summarization

This n8n template demonstrates how to calculate the evaluation metric "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on an incoming Youtube transcript.

The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_summarization_quality

How it works

• This evaluation works best for an AI summary workflows.
• For our scoring, we simply compare the generated response to the original transcript.
• A key factor is to look out information in the response which is not mentioned in the documents.
• A high score indicates LLM adherence and alignment whereas a low score could signal inadequate prompt or model hallucination.

Requirements

• n8n version 1.94+
• Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing