When building an AI agent or LLM-powered feature, the first question we always ask is, "Is it accurate?" We run a few prompts, check the outputs, and high-five when the model returns the "right" answer. But as soon as that agent faces real users in a production environment, a harsh reality sets in: accuracy is not enough.
An agent that's 99% accurate but takes 15 seconds to respond will be abandoned. A RAG pipeline that gives perfect answers but costs a fortune per query will bankrupt the project. An "accurate" response that's riddled with subtle bias can damage your brand's reputation.
To ship better AI, faster, you need to move beyond a simplistic view of accuracy and adopt a multi-faceted evaluation framework. This means systematically testing your AI components against a scorecard of metrics that reflect real-world performance, user experience, and business value.
Relying solely on accuracy is like judging a car only by its top speed. It ignores fuel efficiency, safety, comfort, and reliability. For AI agents, an accuracy-only approach misses critical dimensions:
Mature AI development teams understand this. They don't just ask, "Is it right?" They ask, "Is it the optimal configuration considering quality, performance, and cost?"
To get a holistic view of your agent's performance, you need to track metrics across several categories.
These metrics measure the "correctness" and trustworthiness of the AI's output.
These metrics quantify the speed and resource consumption of your AI system.
These metrics evaluate how well the agent performs its specific job.
Tracking all these metrics across different prompts, models, and RAG configurations can quickly become an overwhelming mess of spreadsheets and one-off scripts. To make data-driven decisions with confidence, you need a structured approach to experimentation.
This is where a dedicated AI experimentation platform like Experiments.do becomes essential. We provide an agentic platform to test, compare, and optimize prompts, models, and RAG pipelines as code.
Instead of manual, ad-hoc testing, you can define rigorous A/B tests with statistical power. For example, let's say you want to compare a complex RAG pipeline against a simpler, finetuned model. You're not just interested in accuracy; you want to know the winner across accuracy, latency, and hallucination risk.
With Experiments.do, you can define this test in a few lines of 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);
});
Our platform handles the rest:
Building great AI products is a game of trade-offs. The path to shipping robust, scalable, and user-loved AI isn't about finding the single most "accurate" model. It's about finding the optimal balance across a scorecard of metrics that matter for your business and your users.
By adopting a systematic AI experimentation process, you can stop guessing and start making data-driven decisions that improve every facet of your AI agent's performance.
Ready to move beyond accuracy and ship better AI, faster? Explore Experiments.do and start running statistically rigorous tests on your AI components today.