Ground truths are often seen as the backbone of AI model validation, but they can also be a roadblock.

What happens if you don't thoroughly test your AI before shipping it? Usually, nothing good.

Companies that avoid the headlines for AI mishaps typically share one key trait: rigorous AI testing practices. The gold standard for this is using "ground truths"— high-quality examples demonstrating ideal outputs. However, not all companies have access to these datasets, and creating them can be expensive and time-consuming.

In this article, we'll cover three effective methods to evaluate your AI models without initial ground truth data, while gathering valuable ground truths over time.

By adopting smarter testing and validation practices, you'll move faster and with greater confidence.

Let’s dive in and explore the trade-offs.

The Three Stages of Ground Truths

When teams begin AI validation, they often ask:

• "How do we quickly create ground truths without spending months or a fortune?"
• "What’s good enough to move forward?"
• "Is synthetic data trustworthy, or are we fooling ourselves?"
• "Can we skip ground truths entirely and still have confidence?"

There's no universal answer, but understanding the ground truth spectrum helps clarify your validation options:

1/ Hand-Made Ground Truths: Ideal but Costly

This is the gold standard. You take the time to manually curate high-quality ground truths to evaluate model performance. These are painstakingly crafted, validated, and expensive in both time and resources. If you have infinite budget and patience, great. But most teams don’t.

2/ No Ground Truths: Pre-Prod Limbo

This is where many teams get stuck. They don’t have ground truths, so they can’t confidently test outputs, and without confident testing, they don’t want to go into production. It’s a Catch-22. This is also where synthetic data often gets introduced as a workaround—more on that in a second.

3/ Actuals: Real-World Feedback from Production

The best validation is actual usage. By deploying your model—even internally—you'll gather realistic, actionable data. Though initially imperfect, even small-scale human reviews significantly enhance validation quality and also help you better understand how users want to use your product in the first place.

The Synthetic Data Trap

Synthetic data sounds like a great compromise. Instead of real user inputs, you generate examples to simulate ground truths. But this can backfire.

A customer once told me our model’s outputs were bad—turns out, they were mistakenly reviewing their own synthetic data instead of our model's outputs. It was a bit awkward (and pretty funny), highlighting the risk & difficulty you take on when using synthetic data.

This happens more than people realize: you spend as much time refining your prompts to get good synthetic ground truths as you would just shipping the app and getting real data.

Practical Strategies to Reach Production Faster

Ground truths don't have to block your progress.. These two strategies can help you validate and move towards confident deployment faster..

Ground truths don't have to block your progress. These two strategies can help you rapidly validate your AI model and move towards confident deployment:

1/ Deploy Internally and Collect Feedback

Instead of stalling in pre-prod, release a version internally and start collecting actual user interactions. Even if it’s just a small batch labeled manually, real-world data will always be more useful than over-engineered synthetic examples.

2/ Use an LLM as a Judge

If you don’t have actuals or ground truths, an LLM can evaluate the quality of outputs based on predefined criteria. It’s not perfect, but it’s better than nothing, and it gives you a scalable way to check output quality without hand-labeling everything.

Ground truths help build confidence, but they shouldn’t block progress

There’s always a balance—enough validation to catch major issues, but not so much that you’re stuck in pre-prod forever.

The key is to know your product, your users, and your risk tolerance.

Shipping an internal tool isn’t the same as shipping an AI feature to end users. And the bar for quality is way higher in legal, medical, or education settings than it is in marketing or creative tools.

You don’t need perfect ground truths to start. Use lightweight checks—internal deployments, evals, or small-scale reviews—to unblock.

Ship something.

Then use real feedback to make it better.