Accuracy is the most overused word in AI.
It sounds scientific, but inside most enterprise systems it hides a lie... accuracy to what?
You can have:
- A customer-support model that’s 95% “accurate” and still frustrates users.
- A retrieval system that’s accurate and still irrelevant.
That’s because most teams optimize for metrics that don’t belong to them. Numbers that look objective, but don’t map to real outcomes.
The fix isn’t better accuracy.
It’s custom evaluation, defining your own success criteria instead of inheriting someone else’s.
Here’s what that looks like in practice:
⚡ Write your own evaluators in plain language that match your domain.
⚡ Run them using your preferred LLM, deterministic rules, or Future AGI’s Turing Models for higher consistency.
⚡ Plug evaluations directly into your CI/CD pipeline and datasets.
⚡ Get actionable feedback, not just numbers but traceable insights tied to business impact.
If your evals can’t tell you when your AI is wrong, your users will, in production.
Define your own “accuracy.”
Automate it.
Make evaluation the foundation of reliability, not a checkbox after deployment.
👉 Try the 5-minute walkthrough: https://shorturl.at/8ABhA
🔗 Or integrate programmatically with our open-source AI Eval Library: https://github.com/future-agi/ai-evaluation
Top comments (0)