Running AI Evaluation Experiments with Evals.do

Unlock Deeper Insights: Compare AI Models and Configurations with A/B Testing

In the rapidly evolving world of artificial intelligence, simply deploying an AI component isn't enough. To truly optimize performance, you need a robust way to understand what's working, what's not, and how different iterations compare. This is where the power of experimentation comes in. With Evals.do, the comprehensive AI evaluation platform, you're not just measuring performance – you're equipped to design and run sophisticated A/B tests and other experiments to drive continuous improvement in your AI functions, workflows, and agents.

Why Experiment with Your AI?

Just like any other software application, AI components benefit immensely from a data-driven approach to development. Running evaluation experiments allows you to:

• Compare Different Models: Have you fine-tuned two versions of a large language model? Do you have different sentiment analysis algorithms? Experiments let you directly compare their real-world performance based on defined metrics.
• Optimize Configurations: Small changes in parameters, prompts, or data pipelines can have significant impacts. A/B testing helps you pinpoint the optimal configurations for your specific use cases.
• Validate New Features: Before rolling out a new AI feature, you can test its impact on key performance indicators without affecting your entire user base.
• Identify Regressions: As your AI evolves, experiments act as a safety net, ensuring new updates don't inadvertently degrade existing performance.
• Quantify Business Impact: Ultimately, experiments help you tie AI performance directly back to business outcomes, demonstrating the ROI of your AI investments.

How Evals.do Facilitates AI Experiments

Evals.do is designed from the ground up to make comprehensive AI evaluation, including experimental setups, intuitive and powerful. Here’s how it works:

1. Define Your Evaluation Criteria

The foundation of any good experiment is clear, measurable criteria. Evals.do allows you to define custom metrics relevant to your AI component's function.

For instance, consider evaluating a customer support agent's responses:

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
    },
    {
      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']
});

In an experimental context, you would apply these same metrics across different versions of your AI to ensure a fair comparison.

2. Set Up Your Experimental Groups (A/B Testing)

With Evals.do, you can easily create different "targets" or configurations for your AI component. For an A/B test, you might have:

• Variant A: Your current production AI model/configuration.
• Variant B: A new proposed AI model/configuration.

You then direct a controlled portion of your dataset (or live traffic, carefully rolled out) to each variant.

3. Leverage Diverse Evaluators

Evals.do supports a multitude of evaluation methods, crucial for rich experimental data:

• Human Review: Essential for subjective qualities like tone, creativity, or nuanced understanding. You can integrate human feedback directly into your experiment workflow.
• Automated Metrics: For objective, quantifiable measures such as latency, token usage, or similarity scores.
• AI-Assisted Evaluation: Use one AI model to evaluate the output of another, speeding up initial screening.

4. Analyze and Compare Results

Once your experiment runs, Evals.do processes the evaluation data from various sources and compiles it into actionable reports. You can then directly compare the performance of Variant A versus Variant B across all your defined metrics. This allows you to conclusively determine which AI version performs better based on your quality standards.

Common AI Experiment Scenarios with Evals.do

• Prompt Engineering Optimization: Test various prompt templates for your LLM-powered applications to find the most effective one.
• Model Fine-tuning Comparisons: Evaluate the impact of different fine-tuning datasets or techniques on your custom models.
• Agent Strategy Improvements: Compare different decision-making strategies or tools integrated into your AI agents.
• Workflow Efficiency: Benchmark different orchestrations of AI components within a workflow for speed, accuracy, and resource consumption.

Get Started with Evals.do

Don't leave your AI performance to chance. With Evals.do, you can go beyond basic monitoring and embrace a rigorous, experimental approach to AI development. Our platform empowers you to define custom evaluation criteria, collect data, and process it through various evaluators (human, automated, AI) to generate performance reports that drive informed decisions.

Ready to elevate your AI component evaluation? Visit evals.do to learn more and start running your first AI evaluation experiment today!

Keywords: AI evaluation, AI performance, workflow evaluation, agent evaluation, AI testing, AI experimentation, A/B testing AI, AI quality, evaluation platform