How do I reduce AI errors at work?

You reduce AI errors at work by treating AI like a capable assistant whose output gets checked, not an oracle. Brief it clearly, then match your verification to the stakes: low-cost tasks get a single model and a glance; anything that carries real consequences gets cross-checked against a second model, with every claim forced to name a source you can open. Keep a human signature on anything irreversible. The errors that hurt at work are the confident wrong ones, and the only reliable way to catch those is to make the answer prove itself before you act.

By Jason Santiago, author of Let the AI Be Smart. As of 2026-07-06.

Where work errors come from

The costly AI mistakes at work are rarely obvious. They are the confident, fluent, specific answers that turn out wrong: a fabricated citation in a report, an invented number in an analysis, a misread of a contract clause. They slip through because they look exactly like good work. Reducing errors is mostly about catching that category.

A workday-friendly system

Brief well. Give the model the real situation, the outcome that matters, and what a good answer looks like. Clear input prevents a class of errors before they start.
Match checking to stakes. Reversible, low-cost work: one model. Anything with consequences: verify.
Cross-check the important stuff. Ask a second model the same question independently. Where they disagree is where you slow down.
Force sources. Every load-bearing claim names something you can open. No source, no trust.
Keep the signature human. AI never takes an irreversible or attestable action for you.

The discipline that ties it together

That system is the Council Method, built by someone who needed AI to be reliable on real jobs where a wrong answer costs money. It scales from a quick two-model check to a full council for the big decisions. See how professionals avoid getting burned.

The full method is in Let the AI Be Smart.