Building effective AI applications and agent flows often feels like a complex process of trial and error. You tweak prompts, try different models, adjust parameters, and hope for the best. But how do you know which changes are truly making a difference? How do you reliably measure the impact of your AI components?
Enter Experiments.do, the platform designed to bring rigorous, data-driven experimentation to your AI and machine learning workflows. Experiments.do provides a structured way to test and iterate on the individual building blocks of your AI systems – the prompts, models, data transformations, and more.
Just like traditional software development benefits from unit and integration testing, AI development demands its own form of validation. Simply deploying an AI component and hoping it works is a recipe for unpredictable results and wasted resources. Testing your AI components allows you to:
Whether you're fine-tuning prompt engineering strategies for your customer support chatbot, comparing different LLMs for content generation, or evaluating feature engineering techniques for a predictive model, Experiments.do provides the framework to do it effectively.
Experiments.do allows you to set up controlled experiments on your AI components. Here's a simplified look at the core concepts:
Think of it as A/B testing, but specifically tailored for the nuances of AI development.
One of the key benefits of Experiments.do is its flexibility and ease of integration. It's designed to fit naturally into your existing development workflow, regardless of your chosen programming language or framework. The core interaction often involves a few lines of code:
import { Experiment } from 'experiments.do';
const promptExperiment = new Experiment({
name: 'Prompt Engineering Comparison',
description: 'Compare different prompt structures for customer support responses',
variants: [
{
// ... your prompt variant 'A' configuration ...
},
{
// ... your prompt variant 'B' configuration ...
},
]
});
// Your code then uses experiment.run(...) to execute a specific variant and collect data
Experiments.do provides SDKs and APIs to make integrating experimentation into your existing code repositories, CI/CD pipelines, and data analysis tools straightforward. This means you can start running controlled experiments on your AI components without significant refactoring of your current codebase.
In the fast-evolving world of AI, relying on intuition alone isn't enough. Experiments.do empowers you to move beyond guesswork and embrace a truly data-driven approach to AI development. By systematically testing and measuring the performance of your AI components, you can:
Ready to bring rigor to your AI workflow? Learn more and get started with Experiments.do.
What is Experiments.do?
Experiments.do helps you define, run, and analyze experiments on different AI components, such as prompt variations, model parameters, or data preprocessing techniques. You can set up variants, define metrics, and collect data to compare their performance.
What types of AI components can I test?
You can test various AI components, including prompt engineering strategies, different large language models (LLMs), model hyperparameters, data augmentation techniques, feature engineering approaches, and more.
How do I define the success of my AI components?
Experiments.do allows you to define custom metrics relevant to your use case, such as response quality, customer satisfaction scores, latency, accuracy, precision, recall, or any other quantifiable outcome.
How do I analyze the results of my experiments?
Experiments.do provides tools for analyzing experiment results, including statistical analysis, visualizations, and comparisons of key metrics across different variants. This helps you make informed decisions about component performance.
How does Experiments.do help me improve my AI's value?
By systematically testing and comparing different approaches, you can identify the AI components and configurations that deliver the best results for your specific business needs, leading to more effective and valuable AI applications.