In the fast-paced world of AI development, it's easy to fall into the "looks good to me" trap. You write a new prompt, test it with a few queries, and it feels better than the old one. So, you ship it. But how much better is it, really? Is it more cost-effective? Is it faster? Does it actually improve user outcomes?
Relying on gut-feeling and subjective evaluation is a risky strategy. As AI systems become more complex—incorporating elaborate prompts, multiple LLM calls, and Retrieval-Augmented Generation (RAG) pipelines—the impact of any single change becomes harder to predict. You might be improving one aspect while unknowingly degrading another, leaving performance, cost savings, and user satisfaction on the table.
It's time to move beyond guesswork. Just as we use rigorous A/B testing to optimize user interfaces and marketing copy, we must apply the same data-driven discipline to our AI components.
The core idea is simple: run controlled, head-to-head experiments to find the optimal configuration for any AI-powered workflow. Instead of guessing which change is best, you let the data speak for itself.
This is the principle behind Experiments.do, an agentic testing platform built for modern AI development. It allows you to systematically iterate on every part of your AI stack, including:
By creating variants and tracking key metrics, you can replace assumptions with statistical certainty.
Let's see how easy it is to set up and run an experiment. Imagine you're trying to improve an automated support bot. You want to see if an empathetic tone leads to better outcomes than a purely direct one.
With the Experiments.do SDK, you can define this test directly in your code:
import { Experiment } from 'experiments.do';
// Define an experiment to compare two different LLM prompts
const exp = new Experiment({
name: 'Support-Response-Prompt-V2',
variants: {
'concise': { prompt: 'Solve the user issue directly.' },
'empathetic': { prompt: 'Acknowledge the user\'s frustration, then solve.' }
}
});
// Run the experiment with a sample user query
const results = await exp.run({
query: 'My login is not working',
metrics: ['cost', 'latency', 'customer_sentiment']
});
console.log(results.winner);
In this example, we:
The platform handles the rest, collecting data for each variant and running a statistical analysis to declare a winner. No more debates or subjective opinions—just clear, actionable data.
Integrating a systematic framework for AI experimentation provides several powerful advantages:
Q: What kind of AI components can I test with Experiments.do?
A: You can A/B test anything from individual prompts and LLM models to entire RAG pipelines and agentic workflows. It's designed for both granular component testing and end-to-end evaluation.
Q: How are experiments evaluated?
A: You define your own success metrics, such as response quality, latency, cost, or custom business KPIs. The platform collects data for each variant and provides statistical analysis to declare a winner.
Q: Can I test different Large Language Models (LLMs)?
A: Yes. Experiments.do is model-agnostic. You can easily set up experiments to compare the performance and cost of models from OpenAI, Anthropic, Google, open-source providers, and more.
Q: How does Experiments.do integrate into my existing workflow?
A: Experiments.do is an API-first platform. You can trigger and manage experiments directly from your codebase using our simple SDK, making it easy to embed continuous, data-driven improvement into your development lifecycle.
The era of building AI on gut-feeling is over. To build best-in-class, efficient, and effective AI services, you need a process that is as sophisticated as the technology itself. Systematic AI experimentation provides the framework to build with confidence and precision.
Ready to take the guesswork out of your AI development? Visit experiments.do to learn more and run your first data-driven experiment.