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Educational Guide

Quality Scoring for LLMs: Building Effective Rubrics

Learn how to create quality scoring systems for LLM outputs. Design rubrics that catch regressions.

Definition

Quality scoring is the process of systematically evaluating LLM outputs against defined criteria to produce numerical scores that enable comparison and threshold-based decisions.

Why Quality Scores Matter

Quality scores enable: 1. **Objective comparison**: Compare prompts and models fairly 2. **Threshold gates**: Block releases below quality bar 3. **Trend tracking**: Monitor quality over time 4. **Team alignment**: Shared definition of 'good'

Designing a Scoring Rubric

Key rubric components: - **Dimensions**: What aspects to evaluate (accuracy, relevance, format) - **Levels**: Score ranges (1-5, 0-100, pass/fail) - **Descriptions**: What each level means - **Weights**: Relative importance of dimensions - **Examples**: Sample outputs for each level

Automated Scoring Approaches

Methods for automation: - **Rule-based**: Regex, keyword matching, format checks - **LLM-as-judge**: Use another LLM to evaluate - **Embedding similarity**: Compare to golden outputs - **Hybrid**: Combine multiple approaches

Setting Thresholds

Choosing quality gates: - Start with baseline measurements - Set thresholds slightly below current performance - Tighten thresholds as quality improves - Use different thresholds for different use cases

Related Topics

LLM Evaluation Metrics

LLM evaluation metrics are quantitative and qualitative measures used to assess the quality, accuracy, and usefulness of large language model outputs.

Prompt Testing Best Practices

A collection of proven methods and workflows for systematically testing LLM prompts to ensure quality, reliability, and safety in production applications.

LLM Output Validation

LLM output validation is the process of checking model outputs for correctness, safety, format compliance, and quality before presenting them to users or using them in downstream systems.

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