Providing Feedback

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Overview

The VLM Run Feedback API enables you to collect and submit feedback on model predictions to continuously improve accuracy and performance. This feedback data is essential for fine-tuning vlm-1 models to better serve your specific use cases and domains.

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Why Feedback Matters

Providing feedback on model predictions serves several critical purposes:

• Model Fine-tuning: Feedback data is used to fine-tune models for improved accuracy on your specific data patterns
• Performance Optimization: Helps identify areas where the model needs improvement
• Domain Adaptation: Enables the model to better understand your industry-specific requirements
• Quality Assurance: Provides a mechanism to flag incorrect or problematic predictions

from vlmrun.client import VLMRun

client = VLMRun(api_key="<your-api-key>")

feedback_response = client.feedback.submit(
    request_id="<request-id>",
    response={
      "name": "John Doe",
      "date_of_birth": "1955-01-01",
      "email": "john@doe.com"
    },
    notes="The extraction was accurate and captured all key information"
)
print(f"Feedback submitted: {feedback_response.id}")

feedback_list = client.feedback.get("<request-id>")
for feedback in feedback_list:
    print(f"Feedback ID: {feedback.id}")
    print(f"Response: {feedback.response}")
    print(f"Notes: {feedback.notes}")

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Fine-tuning with Feedback

The feedback you provide is used to create fine-tuned models that perform better on your specific use cases. This process involves:

1. Data Collection: Feedback is aggregated across your organization
2. Model Training: Fine-tuned models are created using your feedback data
3. Performance Improvement: Updated models show improved accuracy on similar tasks

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Best Practices

• Be Specific: Provide detailed feedback about what was correct or incorrect

• Use Structured Data: Include ratings, categories, and specific metrics when possible

• Add Context: Use the notes field to explain your reasoning

• Consistent Feedback: Maintain consistent criteria across your team for better model training