Fazley RabbiAI language models sometimes state wrong things with complete confidence because they are built to produce likely-sounding text, not to look facts up and verify them. When a model lacks the right information, it may fill the gap with something that sounds correct: a price, a policy, a date, a name, a source. These errors are often called hallucinations. You cannot remove them completely, but you can design your workflows so that wrong answers are rare, easy to spot and caught before they reach a customer.
Why it happens
A language model learns patterns from a very large amount of text and then generates answers word by word, choosing what is likely to come next. That makes it fluent, but fluency is not the same as accuracy. Several things make errors more likely:
- It does not know your business. Unless you give it your price list, stock or return policy, it can only guess.
- It prefers answering to admitting a gap. Newer models are better at saying "I don't know", but the tendency has not disappeared.
- Its knowledge has a cut-off. Anything that changed after training, such as a new rule or a new product, may be missing or out of date.
- Instructions are vague. When a request can be read two ways, the model picks one without telling you.
- The input is long or messy. Details can be missed, mixed up or attached to the wrong item.
The confident tone is the real trap. A wrong answer looks exactly like a right one.
Where it shows up in business
These hypothetical examples show how ordinary the errors can look:
- A Messenger assistant tells a customer that delivery outside Dhaka is free, because nobody gave it the actual delivery charges.
- A meeting summary includes a decision that was discussed but never agreed.
- Invoice extraction swaps the quantity and the unit price on one line.
- A product description says a bag is waterproof when it is only water-resistant.
- A translated return policy subtly changes who pays for the return.
- A report cites a regulation or a source that does not exist.
None of these looks like an error at first glance. That is why the workflow, not the reader's luck, has to catch them.
Design choices that reduce errors
Give it the facts
Ground answers in your own information: price lists, policies, product specifications. Either include them in the prompt or have the system fetch the relevant passages first. The article on retrieval-augmented generation explains how that fetching works, and the company knowledge AI assistant solution applies it to staff questions.
Make "I don't know" an allowed answer
Tell the model exactly what to say when the information is missing, and route those cases to a person. An honest gap is far better than a fluent guess.
Keep each task narrow
"Extract these five fields from this invoice" is safer than "process this invoice". Small, specific jobs are easier to check.
Ask for structure and sources
Fixed fields make missing or odd values stand out. Asking the model to quote the line it relied on lets a reviewer check in seconds.
Check with plain rules
After the AI step, let simple rules test the result. Do the line items add up to the total? Is the product in your catalogue? Is the phone number in a valid format? Is the date plausible?
Keep a person where errors cost most
Anything involving money, health, legal matters or public statements needs a human decision. The article on human review in AI workflows covers how to design that review.
Matching the check to the risk
| Risk | Examples | Sensible check |
|---|---|---|
| Low | Internal notes, headline ideas, first drafts for your own use | The user reads it before relying on it |
| Medium | Customer reply drafts, product descriptions, translations | A staff member approves each one before it goes out |
| High | Prices, refunds, medical or legal content, contracts, financial figures | Grounded data, rule checks and a named person approving every case |
What to tell your team
- AI output is a draft, not a source.
- Check every number, name, date, price and quotation against the original.
- Be most careful when the answer is exactly what you hoped to hear.
- When you find an error, record it. A simple shared list of mistakes is the best material for improving prompts and checks.
A practical next step
Choose one AI output your team already relies on. List the facts in it that must be correct, and for each one, decide how it will be checked: by the source data, by a rule or by a person. If any fact has no check, add one before you scale up. For help designing checks into an automation, see AI and workflow automation.