Building great AI is only half the battle. To truly build AI that actually works and delivers real-world value, you need to rigorously evaluate its performance. Without objective metrics and a structured approach, you're left guessing whether your AI is truly meeting your goals. This is where platforms like Evals.do become indispensable.
Evals.do is designed to help you measure the performance of your AI functions, workflows, and agents against objective criteria. By providing a comprehensive evaluation platform, Evals.do empowers you to make data-driven decisions about which AI components are ready for production and which still need refinement.
Accuracy, precision, and recall are foundational metrics in AI, particularly for classification tasks. But for more complex AI components like agents or workflows, these don't tell the whole story. You need to go beyond the basics and define metrics that reflect the specific task your AI is designed to perform and the impact it should have.
Consider a customer support agent AI. While accuracy of information is crucial, you also need to understand its helpfulness in resolving customer issues and the appropriateness of its tone. These are nuances that traditional metrics often fail to capture.
Evals.do allows you to define custom evaluation metrics tailored to your specific AI components and business goals. This flexibility is key to truly understanding performance. You can move beyond generic benchmarks and focus on what truly constitutes success for your application.
Here's an example of how you might define metrics for a customer support agent evaluation using Evals.do:
import { Evaluation } from 'evals.do';
const agentEvaluation = new Evaluation({
name: 'Customer Support Agent Evaluation',
description: 'Evaluate the performance of customer support agent responses',
target: 'customer-support-agent',
metrics: [
{
name: 'accuracy',
description: 'Correctness of information provided',
scale: [0, 5],
threshold: 4.0 // Define the minimum acceptable score
},
{
name: 'helpfulness',
description: 'How well the response addresses the customer need',
scale: [0, 5],
threshold: 4.2
},
{
name: 'tone',
description: 'Appropriateness of language and tone',
scale: [0, 5],
threshold: 4.5
}
],
dataset: 'customer-support-queries',
evaluators: ['human-review', 'automated-metrics'] // Combine evaluation methods
});
In this example, we've defined accuracy, helpfulness, and tone as key metrics. Each metric has a description, a defined scale for scoring (0-5), and a threshold indicating the minimum acceptable performance level. By setting these thresholds, you establish clear criteria for what constitutes successful performance.
Evals.do understands that evaluating AI often requires more than just automated checks. The platform supports both human and automated evaluation methods, allowing for a truly comprehensive assessment.
By combining these methods, you get a holistic view of your AI component's performance, capturing both the objective and subjective aspects of its effectiveness.
Whether you're building a simple function, a complex multi-step workflow, or an autonomous agent, Evals.do can accommodate your needs. Its flexible architecture allows you to define evaluations for various types of AI components, providing a single platform for all your AI performance measurement efforts.
Moving beyond basic metrics is essential for building AI that truly works in the real world. Evals.do provides the tools and flexibility to define and measure the performance of your AI components against objective criteria. By embracing comprehensive evaluation and making data-driven decisions, you can ensure your AI delivers the value you expect and builds trust with your users.
Ready to start evaluating your AI effectively? Explore Evals.do and unlock the power of comprehensive AI performance measurement.