Building CDDBS — Part 3: Scoring LLM Output Without Another LLM
This article discusses a method for scoring the quality of LLM-generated output without using another LLM. It introduces a 7-dimension rubric that evaluates structural completeness, attribution quality, confidence signaling, evidence presentation, analytical rigor, actionability, and readability.
Why it matters
This approach addresses a critical challenge in deploying LLM-powered applications - ensuring the quality and trustworthiness of the output.
Key Points
- 1LLMs can generate output with high confidence but low accuracy, making it difficult to evaluate quality
- 2The CDDBS approach uses a deterministic rubric to score output based on structural quality rather than accuracy
- 3The 7 scoring dimensions are designed to reward practices that make intelligence products trustworthy
Details
The article explains that the hardest part of using LLM-powered applications is determining whether the output is actually good. Using a second LLM to evaluate the first one has a fundamental flaw - LLMs can be confidently wrong in correlated ways. CDDBS takes a different approach, using a 7-dimension rubric to score the structural quality of the output rather than its accuracy. The rubric evaluates factors like completeness of required sections, quality of evidence attribution, explicit expression of uncertainty, clarity of evidence presentation, analytical rigor, actionability, and readability. This approach is based on an analysis of briefing formats from 10 professional intelligence organizations, which found that only 3 out of 10 use explicit confidence signaling. The article then provides details on how each scoring dimension is implemented in practice.
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