Tackling Hallucinations: Using A/B Testing to Improve Factual Grounding

Large Language Models (LLMs) are notorious for one critical flaw: they can confidently lie. This phenomenon, known as "hallucination," is one of the biggest blockers to deploying reliable, trustworthy AI applications. When your customer support bot invents a refund policy or your research assistant fabricates sources, it doesn't just create a bad user experience—it erodes trust and exposes your business to risk.

The common approach to fixing this is a frantic cycle of "prompt-and-pray." You see a hallucination, tweak your prompt to be "more factual," run a few manual tests, and hope for the best. This is the AI equivalent of guessing.

There's a better way. To build robust, factually-grounded AI, you need to move from guessing to a data-driven, scientific process. You need to run experiments.

The High Cost of the "Guess-and-Check" Trap

When you rely on manual prompt tuning, you’re flying blind. You might fix one instance of a hallucination only to cause another, or you might make your prompt so restrictive that the AI's personality becomes robotic and unhelpful. This ad-hoc process is flawed because it:

• Lacks Statistical Significance: Does your new prompt actually perform better across thousands of requests, or did it just work for the five examples you tested?
• Ignores Trade-offs: Did your "more factual" prompt increase latency or cost? Did it hurt user sentiment? Without tracking metrics, you don't know what you're sacrificing for accuracy.
• Isn't Scalable: As your AI system grows, with more complex RAG pipelines and agentic workflows, manual checking becomes impossible to maintain.

To truly improve factual grounding, you need to systematically test your assumptions. This is where A/B testing for AI comes in.

A Better Way: A/B Testing for Factual Grounding

A/B testing isn't just for button colors anymore. It's the definitive method for optimizing complex AI systems. By running controlled experiments, you can gather empirical data on what actually reduces hallucinations and improves performance. With an AI experimentation platform like Experiments.do, you can test any part of your stack.

Here are three key experiments you can run today to improve your AI's factual grounding.

1. Experiment: Prompt Instruction Optimization

The most direct way to influence an LLM's output is through the prompt. Let's test which instruction is more effective at forcing the model to stick to the facts.

• Hypothesis: A prompt that explicitly forbids invention and requires citing sources will reduce hallucinations compared to a generic prompt.
• Variant A (Control): { prompt: "Use the provided documents to answer the user's question." }
• Variant B (Challenger): { prompt: "Strictly answer the user's question using only the provided documents. If the answer is not in the documents, state 'I do not have enough information to answer.' Cite the document name you used for your answer." }

By running these two variants against a large set of user queries, you can measure which one has a better rate of factual accuracy and a lower rate of hallucination, while also tracking metrics like customer sentiment to ensure the tone remains helpful.

2. Experiment: RAG Configuration Tuning

For most factual Q&A systems, the problem isn't just the LLM—it's the data you feed it. Your Retrieval-Augmented Generation (RAG) pipeline is a prime candidate for experimentation.

• Hypothesis: Using smaller, more focused document chunks for retrieval will provide the LLM with more precise context, leading to more accurate answers.
• Variant A (Control): A RAG configuration with chunk_size: 1024 and retrieving top_k: 3 chunks.
• Variant B (Challenger): A RAG configuration with chunk_size: 512 and retrieving top_k: 5 chunks.

This experiment helps you discover the optimal RAG strategy for your specific dataset, balancing the trade-offs between context quality, cost, and latency.

3. Experiment: Model Comparison

Not all models are created equal. Newer or more specialized models may have better reasoning and grounding capabilities, but they often come at a higher cost. An A/B test is the perfect way to find the sweet spot.

• Hypothesis: A newer model like Anthropic's Claude 3 Sonnet will provide more factually consistent answers than an older model for our specific use case, justifying its cost.
• Variant A (Control): { model: 'gpt-3.5-turbo' }
• Variant B (Challenger): { model: 'claude-3-sonnet-20240229' }

This test gives you the hard data needed to make an informed decision on which model provides the best performance for your budget.

How to Implement AI A/B Testing with Experiments.do

Setting up these experiments is simple with an API-first platform. Experiments.do lets you define and run tests directly in your codebase, integrating continuous improvement into your development workflow.

Here’s how you could implement the prompt experiment from above:

import { Experiment } from 'experiments.do';

// Define an experiment to find the prompt that best reduces hallucinations
const FactualGroundingExp = new Experiment({
  name: 'Support-Bot-Grounding-Prompt-V3',
  variants: {
    'general_prompt': {
      prompt: "Use the provided documents to answer the user's question."
    },
    'strict_prompt': {
      prompt: "Strictly answer using only the provided documents. If the answer isn't there, say 'I do not have enough information.'"
    }
  }
});

// Run the experiment against a suite of test queries
const results = await FactualGroundingExp.run({
  test_queries: [...],
  context_docs: [...],
  metrics: ['cost', 'latency', 'factual_consistency_score']
});

// The winner is determined by the metrics you define
console.log(results.winner); // e.g., 'strict_prompt'

Measuring What Matters: Metrics for Factual Accuracy

Running an experiment is only half the battle. You need to measure the right things. While cost and latency are crucial, for tackling hallucinations, you need more specific metrics:

• Factual Consistency Score: Use another LLM as an evaluator to score whether the response logically follows from the provided context.
• "I Don't Know" Rate: Track how often the model correctly refuses to answer when the information isn't available in the context.
• Citation Accuracy: If you ask the model to cite sources, measure whether the citations it provides are correct.
• User Sentiment: Analyze user feedback to ensure your factually-grounded responses are still perceived as helpful and positive.

Experiments.do allows you to define and track any custom business KPI, giving you a complete picture of each variant's performance.

Build AI You Can Trust

Hallucinations are a solvable problem, but not with guesswork. Building trustworthy, reliable AI requires a commitment to data-driven improvement and rigorous AI experimentation. By A/B testing your prompts, RAG configurations, and models, you can systematically find the optimal components that minimize hallucinations and maximize performance.

Stop guessing and start testing. Your users—and your brand's reputation—will thank you.

Ready to build factually grounded AI? Visit Experiments.do to start running data-driven tests on your AI components today.