Your First AI Experiment: A Practical Guide to Getting Started with Experiments.do

Building effective AI applications often feels like a journey of trial and error. You tweak a prompt, switch a model, or adjust a parameter, hoping for a better outcome. But how do you know if your changes are actually improvements? How do you make those crucial decisions based on data, not just intuition?

Enter Experiments.do, the platform designed specifically for testing and iterating on your AI components with confidence. This post will walk you through setting up and running your very first AI experiment using Experiments.do.

The Challenge of AI Development

The landscape of AI is constantly evolving. New models emerge, prompting techniques become more sophisticated, and the ideal approach for one use case might not be suitable for another. Without a systematic way to test and compare different methods, you're left guessing. This is where AI component testing becomes invaluable.

Testing individual AI components – whether it's a specific prompt structure, a different large language model (LLM), or a particular data preprocessing technique – allows you to isolate the impact of each change. By running controlled experiments, you can measure the performance of different approaches against your defined metrics and make data-driven decisions about which works best.

Why Experiments.do?

Traditional A/B testing tools are great for website features, but they aren't built to handle the unique complexities of AI development. Experiments.do provides a focused platform for:

• Defining clear experiment variants: Easily set up different versions of your AI component to compare.
• Defining relevant metrics: Measure what truly matters for your AI's success, whether it's response quality, accuracy, latency, or a custom business metric.
• Running controlled experiments: Ensure that differences in performance are due to your component variations, not external factors.
• Analyzing results effectively: Get insights into which variants perform best and why, with built-in analysis tools.

This structured approach allows you to rapidly iterate on your AI components, moving beyond guesswork to make data-driven decisions that lead to more effective and valuable AI applications.

Your First AI Experiment with Experiments.do

Let's outline a simple, common AI development scenario and see how Experiments.do can help: comparing different prompt structures for generating customer support responses.

Scenario: You're building an AI assistant for customer support. You want to test two different ways of prompting an LLM to generate initial responses to customer inquiries.

Experiment Goal: Determine which prompt structure results in higher quality and more helpful customer support responses.

Components to Test: Two different prompt variations.

Metrics to Track:

• Response Quality: A subjective rating (e.g., on a scale of 1-5) based on human evaluation.
• Helpfulness: Another subjective rating (1-5) by a human evaluator.
• Latency: Time taken to generate the response.

Setting up the Experiment in Experiments.do:

Experiments.do provides a straightforward way to define your experiments. Here's a simplified look at how you might set this up using the platform's structure (similar to the code example provided):

import { Experiment } from 'experiments.do';

const promptExperiment = new Experiment({
  name: 'Customer Support Prompt Comparison',
  description: 'Compare different prompt structures for customer support responses',
  variants: [
    {
      name: 'Prompt A',
      description: 'Basic, concise prompt structure',
      // Define your specific prompt string here
      prompt: 'Please provide assistance for the following customer inquiry: [customer_inquiry]'
    },
    {
      name: 'Prompt B',
      description: 'More detailed prompt structure with persona',
      // Define your specific prompt string here
      prompt: 'Act as a friendly and helpful customer support agent. Respond to the following inquiry clearly and politely: [customer_inquiry]'
    },
  ],
  // Define your desired metrics here
  metrics: [
    { name: 'response_quality', type: 'rating' }, // Or choose appropriate metric types
    { name: 'helpfulness', type: 'rating' },
    { name: 'latency', type: 'duration' },
  ],
  // You would then integrate this into your application code
  // to run the experiment and log the results for each variant
});

Running the Experiment:

Once defined, you integrate the experiment into your application code. As customer inquiries come in, your application would consult Experiments.do to randomly assign a user to either "Prompt A" or "Prompt B". You would then generate the response using the assigned prompt and log the results (response quality, helpfulness ratings from human evaluators, and latency) back to Experiments.do for that specific experiment run and variant.

Analyzing the Results:

Experiments.do collects and organizes the data from each experiment run. The platform's analysis tools allow you to:

• Compare the average response quality and helpfulness ratings for Prompt A vs. Prompt B.
• See if there's a statistically significant difference in performance between the variants.
• Identify any trade-offs, like if one prompt is higher quality but has higher latency.

Based on this analysis, you can confidently choose the prompt structure that delivers the best results for your customer support use case.

Beyond Prompt Engineering

While our example focuses on prompt engineering, Experiments.do is versatile. You can use it to test:

• Different LLMs: Compare the performance of various models (e.g., GPT-4, Claude, open-source models) for a specific task.
• Model Parameters: Experiment with different settings for your model's hyperparameters.
• Data Preprocessing: Evaluate the impact of different data cleaning or augmentation techniques.
• Agentic Workflow Components: Test individual steps or different orchestrations within a larger AI workflow.

By systematically testing these individual components, you gain a deeper understanding of what drives your AI's performance and how to optimize it.

Getting Started

Ready to stop guessing and start testing your AI components with data? Head over to Experiments.do and start defining your first experiment. The platform provides the tools you need to bring rigor and confidence to your AI development process. Define your variants, set your metrics, run your experiments, and start building AI that truly delivers value.

#AIComponentTesting #AIExperimentation #AIValidation #PromptEngineeringTesting #ModelTesting #AIDevelopment #MachineLearningExperiments #AIWorkflow #AgenticWorkflow