How to A/B Test LLM Prompts Like a Pro with Experiments.do

Prompt engineering often feels more like an art than a science. You tweak a word here, rephrase a sentence there, and run it a few times, hoping for a better result. But how do you know if your new prompt is truly an improvement? Does it just "feel" better, or does it actually drive better outcomes for your users and your business?

Without data, you're flying blind. A prompt that seems more creative to you might be more confusing to your users. A more detailed prompt might produce higher-quality answers but be prohibitively slow or expensive at scale.

This is where data-driven AI experimentation comes in. Instead of guessing, you can run controlled A/B tests on your AI components to find the optimal configuration. Today, we'll walk you through how to stop guessing and start winning with Experiments.do, the agentic testing platform for AI.

This practical, step-by-step tutorial will show you how to set up your first prompt A/B test, define what success looks like, and confidently deploy the winning version.

Why A/B Test Your Prompts?

In traditional software, A/B testing landing pages or button colors is standard practice. The same rigor is now essential for building reliable AI. This is the core principle of LLM Evaluation: moving from subjective feelings to objective metrics.

By A/B testing your AI, you can:

• Quantify "Better": Replace "I think this prompt sounds more helpful" with "The 'empathetic' prompt variant increased customer sentiment scores by 15%."
• Optimize for Business Goals: Is your goal the absolute highest quality, the lowest cost, the fastest response time, or a delicate balance of all three? Testing allows you to make informed trade-offs.
• De-risk Changes: Before rolling out a new prompt to 100% of your users, you can validate its performance and ensure it doesn't cause unexpected regressions in quality, cost, or latency.
• Accelerate Iteration: Quickly compare dozens of ideas—from simple wording changes to entirely different prompting strategies—and let the data guide your development.

A Step-by-Step Guide to Your First Prompt Experiment

Let's imagine we're building an AI-powered customer support bot. Our goal is to resolve user issues effectively. We have a hypothesis: An empathetic prompt that first acknowledges the user's frustration will lead to higher user satisfaction than a prompt that just solves the problem directly.

Time to test it with Experiments.do.

Step 1: Install and Import the SDK

First, get the Experiments.do library into your project.

npm install experiments.do

Then, import it into your code.

import { Experiment } from 'experiments.do';

Step 2: Define Your Experiment and Variants

Next, we define our experiment. We'll give it a descriptive name and then outline our two competing variants: concise and empathetic. This tells the platform which two prompts to compare.

// 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.' }
  }
});

Here, 'concise' is our control (the original prompt), and 'empathetic' is our challenger.

Step 3: Define Your Success Metrics

This is the most crucial step in any form of AI Experimentation. How do we define a "winner"? For our support bot, we care about a few things:

• customer_sentiment: Is the user happy with the response? (Our primary metric)
• cost: How much did the LLM call cost?
• latency: How long did it take to get a response?

We pass these into the run method.

Step 4: Run the Experiment

Now, we execute the experiment with a sample user query. The Experiments.do SDK handles the magic behind the scenes: it runs both prompt variants against the LLM, collects the data for your defined metrics, and prepares the results.

// Run the experiment with a sample user query
const results = await exp.run({
  query: 'My login is not working',
  metrics: ['cost', 'latency', 'customer_sentiment']
});

Step 5: Analyze the Results and Ship the Winner

After running the experiment across a statistically significant number of user queries, the platform analyzes the results for each metric. It then declares a winner.

// The 'winner' is determined based on statistical analysis of the metrics you defined
console.log(results.winner); 
// Possible output: 'empathetic'

If the data shows that the empathetic variant consistently leads to higher sentiment without an unacceptable increase in cost or latency, you have a clear, data-backed winner. You can now confidently update your production prompt, knowing you've made a measurable improvement.

Beyond Prompts: The Universe of A/B Testing AI

Prompt Engineering is just the beginning. The real power of a platform like Experiments.do is its ability to test every layer of your AI stack.

• A/B Test Models: Is gpt-4o worth the extra cost over claude-3-sonnet for your specific use case? Set up an experiment with the same prompt and different models to get a definitive, cost-performance answer.
• RAG Optimization: Does adding a new vector database or changing your chunking strategy actually improve retrieval accuracy? Test your entire RAG Optimization pipeline to measure the impact on final answer quality.
• Full Agentic Workflows: Compare two completely different multi-step agent behaviors. Does an agent that asks clarifying questions outperform one that tries to answer immediately? Test it!

Stop Guessing, Start Experimenting

Moving from intuition-based tweaks to data-driven A/B Testing AI is the single most important step you can take to level up your AI development. It's how you build Services-as-Software that are not only powerful but also efficient, reliable, and continuously improving.

Ready to run your first data-driven AI experiment? Get started with Experiments.do today and find the optimal configuration for any AI-powered workflow.