AI Hallucinations: What Are They and How Can You Prevent Them?

What are AI hallucinations?

Suppose we ask AI for the local company-register IDs of 20 businesses in a major city whose revenue is growing. It may return a seemingly logical, complete list—but every registration number could be made up.

What makes hallucinations particularly tricky is that appearance of logic. The answers look convincing, and the AI expresses no doubt. That is why language-model interfaces remind users to verify the figures and facts they provide.

What causes AI hallucinations?

Hallucinations are especially likely in three situations:

• missing data (the main cause: the model guesses);
• an imprecise prompt (the model fills in the gaps);
• an overly complex task (the model tries to do everything at once).

The issue is not just the language model itself but its access to data. Hallucinations are not entirely random or beyond our control: they arise under conditions we can address.

How can you prevent AI hallucinations?

Giving AI direct, structured access to reliable data can help prevent hallucinations. With that foundation, language models are better equipped to interpret information, make decisions and draw conclusions.

The following three methods provide that access at different levels of technical complexity.

Prepare a data file

Providing ready-to-use data is the simplest way to tackle hallucinations. It leaves the AI less room to invent details.

New users sometimes treat a language model as a search engine, an ETL pipeline and a data-scraping system rolled into one. But when a task involves collecting, cleaning and standardising a large amount of data, it can exceed the model’s context window, sharply increasing the risk of hallucinations.

That is why it is worth breaking data collection into several smaller prompts and checking the AI’s output as you go. It may sound tedious, but it creates a solid foundation for what language models do best: working with data they have been given.

Use an API

Connecting to an API is a more advanced approach: it offers faster, more scalable access to data that can stay up to date. If preparing a data file is like handing the model a bucket of water, an API is like giving it access to a tap.

The web searches used by ChatGPT, Claude and Gemini are not unlike human searches. Language models retrieve content from external sites, such as Reddit or Wikipedia, and use it to formulate an answer.

With this approach, however, you cannot be sure the content is accurate or current. Sources may use different standards and dates, and may contain human errors or opinions. An API can address these weaknesses by providing a consistent, programmatic source of cleaned data.

For example, the Monitly API provides access to statistics from around the world through direct connections to sources such as national statistics offices, Eurostat, the World Bank, WHO and the IMF. Instead of searching across multiple websites, AI can query the API for a current result, making the data easier to analyse and interpret.

Connect through MCP

Connecting AI through MCP (Model Context Protocol) gives language models direct access to files, spreadsheets, code or applications. This can significantly reduce the risk of hallucinations by supplying data the model can work with.

For example, the company-data platform Compabase offers an MCP connection that lets AI quickly verify local companies. If we ask for the local company-register IDs of 20 businesses in a major city with growing revenue, the model can return a consistent, verified list instead of inventing details. The difference is not that the model has suddenly become “smarter”. It previously lacked access to the data; now it can run a straightforward SQL query against the database and retrieve the results.

In other words:

Without MCP:

• some companies may not exist;
• the financial data may be unreliable;
• the company-register IDs may be fabricated.

With MCP:

• the model queries a database;
• it returns specific records;
• the query result is deterministic.

Of course, access to MCP does not eliminate hallucinations if we still ask the model to process and combine more information than it can reliably handle. But when data is structured, kept current and leaves less room for interpretation, AI can spend less time guessing and more time analysing what is actually there.