Seeking Insights on Implementing Weighted Scoring in API for Transcript Analysis

I’m currently exploring an API implementation for evaluating transcripts, and part of this involves assigning weighted scores to various components of a communication analysis. My friend created a prompt that uses weighted analysis.

The prompt includes the weighting as follows:

  1. Active Listening: 15%
  2. Verbal Acknowledgement: 15%
  3. Non-Verbal Cues: 10%
  4. Reflecting and Paraphrasing: 15%
  5. Emotional Resonance: 15%
  6. Validation: 10%
  7. Supportive Interventions: 10%
  8. Consistency: 10%

The goal is to have the API provide a nuanced summary by effectively balancing these elements according to their assigned weights. My question is about the feasibility and accuracy of such a weighting system when implemented through an AI model. Can an AI reliably apply these specific weightings to generate balanced, insightful summaries?

If anyone has experience or insights with similar implementations, your input would be immensely valuable. I’m particularly interested in understanding the limitations and potential challenges in ensuring that the AI adheres to these weightings accurately.

Any shared experiences, tips, or suggestions are greatly appreciated!