Scorer

This document explains the Scorer node, which assigns numerical scores (0-100) to items based on custom criteria.

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Node Inputs

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Required Fields

• Item: The content to be scored
• Criteria: Rules for scoring (e.g., “Clarity: 0-30, Grammar: 0-40, Relevance: 0-30”)

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Optional Fields

• Include Justification: Get AI’s reasoning for scores
• Additional Context: Extra guidance for scoring
• Temperature: Controls scoring consistency (0-1)
  • 0: More focused, consistent
  • 1: More creative, varied
• Cache Response: Save responses for reuse

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Show As Input

The node allows you to configure certain parameters as dynamic inputs. You can enable these in the “Configure Inputs” section:

• item: String

  • The text or item to be scored
  • Example: “Customer feedback response”

• criteria: String

  • The scoring criteria or rubric
  • Example: “Score based on clarity, politeness, and helpfulness”

• Additional Context: String

  • Extra information to help with scoring
  • Example: “This is feedback from a premium customer”

• include_justification: Boolean

  • true/false to include explanation for the score
  • When enabled, provides reasoning for the assigned score

• model_preference: String

  • Name of the AI model to use
  • Accepted values: “Claude 3.7 Sonnet”, “Claude 3 Haiku”, “GPT-4o”, “GPT-4o Mini”, etc.

• Cache Response: Boolean

  • true/false to enable/disable response caching
  • Helps reduce API calls for identical inputs

• Temperature: Number

  • Value between 0 and 1
  • Controls scoring consistency

When enabled as inputs, these parameters can be dynamically set by previous nodes in your workflow. If not enabled, the values set in the node configuration will be used.

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Node Output

• Score: Numerical value between 0-100
• Justification: AI’s scoring explanation (if enabled)

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Node Functionality

The Scorer node:

• Analyzes content against criteria
• Assigns numerical scores
• Provides scoring rationale
• Handles batch scoring
• Ensures consistent evaluation

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Available AI Models

• Claude 3.7 Sonnet
• Claude 3.5 Haiku
• OpenAI o1
• OpenAI o3 mini
• GPT-4o
• GPT-4o Mini
• DeepSeek V3
• DeepSeek R1
• Gemini 2.0 Flash
• Grok 2
• Azure OpenAI
• And more

Note: Auto-Select uses a third-party model routing service and automatically chooses the appropriate model for cost, performance, and availability. Not ideal if consistent model behavior is needed.

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AI Model Selection Guide

When choosing an AI model for your task, consider these key factors:

Additional selection factors:

• Task complexity and required accuracy
• Response time requirements
• Cost considerations
• Consistency needs across runs
• Specialized knowledge requirements

For more detailed information on AI models with advanced reasoning capabilities, you can refer to:

• Anthropic Models Overview
• Anthropic Extended Thinking Documentation
• OpenAI Reasoning Guide
• OpenAI o1 Models

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Common Use Cases

1. Content Quality:

Criteria: 
- Writing clarity (0-30)
- Accuracy (0-40)
- Engagement (0-30)

2. Support Responses:

Criteria:
- Politeness (0-25)
- Problem solving (0-50)
- Response time (0-25)

3. Product Reviews:

Criteria:
- Detail level (0-30)
- Helpfulness (0-40)
- Objectivity (0-30)

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Loop Mode

Input: List of items to score
Process: Score each against criteria
Output: Scores and justifications for each item

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Important Considerations

1. Expert models (OpenAI o1) cost 30 credits, advanced models (GPT-4o & Claude 3.7) cost 20 credits, and standard models cost 2 credits per run
2. You can drop the credit cost to 1 by providing your own API key under the credentials page
3. Define clear, measurable criteria for accurate output
4. Enable justification for transparency

In summary, the Scorer node helps quantify quality and performance using AI-powered assessment against your custom criteria.