Why AI Gets Things Wrong, and How to Catch It

Every AI assistant sometimes states false things with total confidence. This is not a rare glitch or a sign that one product is broken. It follows from how language models work. The good news is that you can reduce errors a lot and catch most of the rest with a simple routine.

The main causes

• Plausible text, not checked facts. The model generates text that sounds right. When it lacks the real answer, it may still produce something that fits the pattern, such as a believable citation or statistic.
• Outdated knowledge. A model's training stops at some point. Without a search tool it may be unaware of newer events, products or laws.
• Missing context. If your question depends on details only you know, the model fills gaps with guesses.
• Ambiguous questions. Unclear wording leads to an answer to a slightly different question.
• Long or messy conversations. Important details get diluted or dropped, and earlier errors get repeated.

Where errors cluster

Be extra careful with:

• Quotes, citations, case law, paper titles and links
• Specific numbers, dates and names
• Niche topics with little written about them
• Calculations done in your head instead of with a tool
• Recent events and anything that changes often

Ways to prevent errors

1. Provide the source. Paste the text and say "answer only from the text below". Models are far more accurate when working from supplied material.
2. Allow "I don't know". Add: "If the answer is not in the material or you are unsure, say so."
3. Use search or retrieval features. Assistants with web access can cite pages. Open the pages yourself, because a citation can still be misread or misattributed.
4. Ask for reasoning on hard problems. Step-by-step work exposes weak links.
5. Use code for math. Many assistants can run code. Ask for a calculation to be done that way.
6. Keep questions narrow. One clear question beats a sprawling one.

A verification routine

For anything you will publish, send or act on:

1. Ask the assistant to list the factual claims in its answer.
2. Mark which ones matter if wrong.
3. Check those against primary sources: official sites, original documents, your own data.
4. Ask a second time in a new chat, or on a different assistant, and compare. Agreement is not proof, but disagreement is a useful warning.

Handy prompts

List every factual claim in your last answer as a numbered list. For each, rate your confidence as high, medium or low, and say what source I should check.

Answer only using the document below. Quote the sentence that supports each point. If the document does not say, write "Not stated".

Do not argue it into agreement

If you say "Are you sure?", some assistants flip to a different answer even when the first was right. Instead, ask it to re-check specific steps or provide evidence. Treat a changed answer as a prompt to verify, not as a ruling.

Match care to stakes

A brainstorm for party names needs no fact check. A medical, legal, financial or safety question does. In those areas, use AI to understand the topic and prepare good questions, and rely on a qualified professional for decisions.

What improving models change

Newer models tend to make fewer errors, yet none are error free. Tools such as search and code execution help, but they add their own failure modes. The habits above stay useful however capable assistants become.

Summary

• Expect occasional confident mistakes.
• Supply sources and permit uncertainty.
• Verify the claims that carry weight.
• Scale your checking to the stakes.