Evaluation

Automated AI quality control at scale. Handit.ai’s evaluation system provides comprehensive quality assessment for your AI models using advanced LLM-as-Judge technology, delivering instant feedback on model performance through our intuitive platform interface.

Transform your AI quality control from reactive spot-checking to proactive, comprehensive monitoring.

The Manual Review Challenge

Picture this: Your AI chatbot handles thousands of customer inquiries daily. You suspect quality is declining, but manually reviewing responses is overwhelming. You randomly check 50 conversations and find issues—but what about the other 4,950 interactions?

Manual evaluation doesn’t scale. You need:

• Consistency - Different reviewers have different standards
• Coverage - You can’t manually check every interaction
• Speed - By the time you review, the damage is done
• Objectivity - Human reviewers bring unconscious bias
• Specificity - Understanding exactly what needs improvement

Why AI Evaluation Matters

LLM-as-Judge Technology

Leverage powerful language models to assess the quality, accuracy, and relevance of your AI outputs with human-level understanding.

How it works:

• Model integration - Connect GPT models and Llama (via Together AI) through the platform
• Automated evaluation - Run on specified percentage of your production traffic
• Real-time insights - Get quality scores within seconds through the dashboard
• Historical tracking - Monitor quality trends and improvements over time

Single-Purpose Evaluator Framework

The key to effective AI evaluation is focus. Each evaluator should assess one specific quality dimension for maximum clarity and actionable insights.

Why Single-Purpose Works Better

❌ What Doesn’t Work:

One evaluator checking: helpfulness + accuracy + tone + format + compliance
Result: Vague scores, unclear what needs improvement

✅ What Works:

Completeness Evaluator: Does the response address all parts of the question?
Accuracy Evaluator: Is the information factually correct?
Format Evaluator: Does it follow the required structure?
Empathy Evaluator: Does it show understanding for the user's situation?

Result: Clear, actionable insights on exactly what to improve

Platform Workflow

Everything happens through the intuitive Handit.ai platform interface:

1. Connect Model Tokens

Add your evaluation models and test connections:

• GPT-4o models (OpenAI)
• Llama v4 models (Together AI)
• Test connections and save configurations

2. Create Focused Evaluators

Build single-purpose evaluators in the Evaluation Suite:

• Define specific evaluation prompts
• Associate with appropriate model tokens
• Focus on one quality dimension per evaluator

3. Associate to LLM Nodes

Connect evaluators to your AI functions:

• Link evaluators to specific AI functions
• Set evaluation percentages (5-15% recommended to start)
• Configure priorities for different quality aspects

4. Monitor & Analyze

View comprehensive quality insights:

• Real-time evaluation results
• Quality trends across different aspects
• Actionable insights and patterns

Quality Dimensions

Customer Service Evaluators

• Completeness - Does the response address all parts of the question?
• Empathy - Does it show understanding and care?
• Solution Clarity - Are instructions clear and actionable?
• Policy Compliance - Does it follow company guidelines?
• Escalation Appropriateness - When should it suggest human help?

Technical Support Evaluators

• Technical Accuracy - Is the technical information correct?
• Safety Check - Are suggested actions safe?
• Troubleshooting Flow - Does it follow logical diagnostic steps?
• Solution Completeness - Does it provide a full resolution path?
• Risk Assessment - Are there potential negative consequences?

Content Generation Evaluators

• Brand Alignment - Does it match company voice and values?
• Factual Accuracy - Is information correct and current?
• Format Compliance - Does it follow required structure?
• Engagement Level - Is it compelling and interesting?
• Target Audience - Is it appropriate for intended readers?

Business Impact Tracking

Connect evaluation results to real business outcomes:

• Quality measurement - Track specific quality dimensions with precision
• Business metric correlation - Link evaluator scores to customer satisfaction and conversions
• Data-driven insights - Understand which quality aspects matter most for your business
• Trend analysis - Monitor quality patterns over time for each dimension
• Optimization foundation - Evaluation data provides the foundation for AI system optimization

Supported Models

OpenAI Models

• GPT-4o - Highest accuracy for complex evaluations
• GPT-3.5-turbo - High-volume evaluation with good performance
• GPT-4-turbo - Balanced performance and speed

Together AI (Llama Models)

• Llama v4 Scout - High-quality open source alternative
• Llama v4 Maverick - Faster processing for high-volume needs
• CodeLlama - Specialized for technical/code evaluation

Next Steps

Ready to implement automated quality control for your AI systems?

• Get Started with Evaluation Quickstart
• Learn about Evaluation Features
• Explore Integration Options

Get Started

Ready to implement automated evaluation? Start with our quickstart guide:

Need help setting up evaluations? Check out our GitHub Issues  or Contact Us.