Marketing

Judging Prospect Fit with a Rubric and Model

• Batch Size and Parallelism: Latency and Cost
• Calibrating a Model Judge Against Human Labels
• Calibrating a Model Scorer Against a Human Judge
• Keeping a Scoring Prompt in Sync Across Two Models
• When to Audit the Human Judge: Checking the Reference Standard
• A Drift Monitor for Model Scorers: Logging Scores and Rechecks
• A Fallback Ladder for Classifier Failures
• A Reference Set That Survives a Taxonomy Change
• A Score Ledger for Honest Model Comparisons
• Auditing the Labels You Calibrated On
• How Many Human Judgments You Need to Trust a Model Scorer
• How to Use a Model to Score a Cold List Before You Write
• How to Write a Tie-Break Rule for Your Rubric
• Logging Handoff Notes So You Can Audit Who Saw What
• Scoring Prospect Fit with a Model and a Rubric
• Separating Model Error from Rubric Noise
• Validating Model JSON in Your Code Before It Reaches a Scorer
• What a 1,000-Row Scoring Run Costs in Tokens and Minutes
• What a Handoff Costs Reviewers: Reading Time vs. Context Volume
• When a Model Should Skip a Row Instead of Forcing a Draft
• When a Refusal Should Trigger a Fallback Model, Not a Human
• When Scorers Agree but Do Not Predict Outcomes
• When the Reference Standard Is a Model, Not a Person
• When to Batch, When to Stream: A Decision Table for Scoring Jobs