The world of AI development often feels like a wild frontier. We tweak a prompt here, swap a model there, and eyeball the results, hoping we've stumbled upon a "better" version. This ad-hoc process is slow, unreliable, and impossible to scale. It leads to brittle applications, unexpected regressions, and a nagging uncertainty: are we really improving our AI, or just getting lucky on a few cherry-picked examples?
To build production-grade AI that users can trust, we need to move beyond gut-feel and embrace engineering discipline. The new paradigm for achieving this is Experiments-as-Code.
Just as Infrastructure-as-Code (IaC) brought versioning, automation, and reproducibility to cloud deployments, Experiments-as-Code (EaC) brings the same rigor to AI validation. It's the practice of defining your AI tests—your prompts, model comparisons, and RAG configurations—as declarative code artifacts that live right alongside your application code.
This is the core principle behind Experiments.do, an agentic platform designed to help you ship better AI, faster.
Let's be honest: traditional prompt engineering is often a mess of browser tabs, spreadsheets, and manual comparisons. It’s a process fraught with cognitive bias and lacking in statistical significance. How do you know your "improved" prompt won't degrade performance on edge cases you haven't thought of?
With an Experiments-as-Code approach, you replace guesswork with a data-driven workflow. You can systematically test and validate every component of your AI stack to find the optimal configuration.
Consider this common scenario: you want to know whether a Retrieval-Augmented Generation (RAG) pipeline using a general-purpose model is better than a fine-tuned model for answering questions about your product. Instead of a messy manual test, you can define a structured experiment in code:
import { Experiment } from 'experiments.do';
const RAGvsFinetune = new Experiment({
name: 'RAG vs. Finetuned Model',
description: 'Compare retrieval-augmented generation against a finetuned model for product Q&A.',
variants: [
{
id: 'rag_pipeline',
agent: 'productExpertAgent',
config: { useRAG: true, model: 'gpt-4-turbo' }
},
{
id: 'finetuned_model',
agent: 'productExpertAgent',
config: { useRAG: false, model: 'ft:gpt-3.5-turbo-product-qa' }
}
],
metrics: ['accuracy', 'latency', 'hallucination_rate'],
sampleSize: 1000
});
RAGvsFinetune.run().then(results => {
console.log(results.winner);
});
This simple object codifies the entire A/B test. It clearly defines:
By running this experiment, you get a clear, data-backed winner, eliminating ambiguity and enabling confident decision-making.
Effective AI experimentation goes far beyond prompt engineering. The prompt is just one variable in a complex system. True optimization requires testing across the entire AI stack. With a platform like Experiments.do, you can easily design tests for:
By treating each of these components as a variable in an experiment, you can systematically identify bottlenecks, improve quality, and fine-tune the cost-performance ratio of your entire AI service.
A change that looks promising on five examples can easily fail across 1,000 diverse inputs. Making high-stakes decisions based on small, anecdotal samples is a recipe for building unreliable products.
This is where an agentic experimentation platform becomes indispensable. It handles the heavy lifting of:
You move from "I think variant B feels better" to "We have 95% confidence that variant B improves accuracy by 10% without a significant impact on latency." This is the language of professional engineering, and it's essential for building AI systems that are safe, reliable, and effective.
The future of AI development is structured, data-driven, and reproducible. The era of building production AI on gut feelings is over. By adopting an Experiments-as-Code paradigm, you embed quality and reliability directly into your development lifecycle. You create a system of continuous improvement where every change can be validated, and every decision is backed by data.
Ready to ship better AI, faster? Explore Experiments.do and bring statistical rigor to your AI development.
What is AI experimentation?
AI experimentation is the process of systematically testing different versions of AI components—like prompts, models, or retrieval strategies—to determine which one performs best against predefined metrics. It allows you to make data-driven decisions to improve your AI's quality and reliability.
How does Experiments.do simplify A/B testing for AI?
Experiments.do allows you to define experiments as simple code objects. You specify your variants (e.g., different prompts or models), metrics, and sample size, and our agentic platform handles the test execution, data collection, and statistical analysis, providing you with clear results via an API.
Can I test more than just prompts?
Absolutely. With Experiments.do, you can create experiments for any part of your AI stack, including different LLM models (e.g., GPT-4 vs. Claude 3), RAG configurations, function-calling tools, or entire agent workflows.
How are results measured and analyzed?
You define key business and quality metrics for each experiment. Our platform collects data for each variant and performs statistical analysis to determine a winning version with confidence levels. Results are delivered via API or our dashboard for easy integration and review.