Evaluation
Your autonomous engineer needs to know what’s broken to fix it. Handit’s evaluation system provides comprehensive quality assessment that powers autonomous issue detection and fix generation.
The Manual Review Problem
Picture this: Your AI handles thousands of interactions daily. You suspect quality is declining, but manually reviewing responses is overwhelming. You check 50 conversations, find issues, and wonder about the other 4,950 interactions you didn’t see.
Manual evaluation doesn’t scale. By the time you notice problems, they’ve already impacted users. You end up playing whack-a-mole with quality issues instead of systematically improving your AI.
Handit solves this by using AI to evaluate AI—at scale, consistently, and with focused insights that power automatic issue detection and fix generation.
Your autonomous engineer evaluates every interaction, detects quality patterns, and generates targeted fixes based on comprehensive quality data.
How Evaluation Powers Your Autonomous Engineer
Issue Detection: By monitoring quality scores continuously, your autonomous engineer identifies when performance drops and what patterns indicate problems.
Root Cause Analysis: Detailed evaluation data helps your autonomous engineer understand whether issues stem from prompt problems, logic errors, or missing context.
Fix Validation: Before creating pull requests, your autonomous engineer tests improvements against evaluation data to ensure fixes actually work.
Continuous Learning: As fixes get deployed, new evaluation data shows their effectiveness, helping your autonomous engineer improve over time.
Focused Evaluation Approach
The key to effective evaluation is focus. Handit uses single-purpose evaluators that examine one specific quality dimension at a time.
Example evaluators:
- Completeness: “Does the response address all parts of the question?”
- Accuracy: “Is the information factually correct?”
- Empathy: “Does the response show understanding for the user’s situation?”
Best Practice: Create separate evaluators for each quality dimension. This provides actionable insights and enables your autonomous engineer to generate targeted fixes.
The result: When empathy scores drop, your autonomous engineer knows exactly what to focus on when generating improvements.
Supported Models
OpenAI: GPT-4o (highest accuracy), GPT-3.5-turbo (cost-effective) Together AI: Llama v4 Scout (open source), Llama v4 Maverick (high-volume)
Getting Started
Ready to give your autonomous engineer the evaluation data it needs?
Main Quickstart - Includes Evaluation Evaluation SetupAdvanced: Custom Evaluators, LLM as Judges
Transform your AI quality control from manual spot-checking to autonomous monitoring and fixing.