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

What are AI hallucinations?

For example, ask an AI tool for the Companies House registration numbers of 20 London companies with growing revenues. It may return a seemingly coherent, complete list — but the registration numbers could all be invented.

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

Why do AI hallucinations happen?

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 what data it can access. Hallucinations are not necessarily random or beyond our control: the conditions that make them more likely can be addressed.

How can you prevent AI hallucinations?

Giving AI direct, structured access to reliable data can help prevent hallucinations. Language models are best placed to interpret information, make decisions and draw conclusions when they have relevant material to work with.

The following three approaches offer ways to do that, depending on your needs and technical experience.

Prepare a data file

Providing a ready-to-use dataset is the simplest way to reduce the scope for invented answers. It gives the AI less room to fill gaps with a story of its own.

New users sometimes treat a language model as a search engine, an ETL pipeline and a web scraper rolled into one. But if a task involves collecting, cleaning and standardising large amounts of data, it can exceed the model’s context window and sharply increase 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 sound foundation for what language models do well: working with data they have actually been given.

Use an API

Connecting to an API is a more advanced approach: it provides faster, more scalable access to data that can also be kept 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 so different from the way a person searches. Language models retrieve material from external sites, such as Reddit or Wikipedia, then use it to form an answer.

With that approach, however, you cannot be sure the material is accurate or current. Dispersed sources may use different standards and dates, and may contain human errors or opinions. An API can address this weakness by providing a consistent, programmatically accessible source of prepared data.

For example, the Monitly API provides access to statistics from around the world through direct connections to sources such as the Office for National Statistics, Eurostat, the World Bank, WHO and the IMF. Rather than searching multiple websites, an AI tool can query the API for the latest available result, making the data easier to analyse and interpret.

Connect through MCP

Connecting AI through MCP (Model Context Protocol) can give language models direct access to files, spreadsheets, code and applications. By making relevant data available, it can substantially reduce the risk of hallucinations.

For example, the company-data platform Compabase has introduced an MCP integration that lets AI verify local companies quickly. Ask for the Companies House registration numbers of 20 London companies with growing revenues, and the model can return a consistent, verified list instead of inventing details. It has not suddenly become ‘smarter’: it now has access to the data. It can run a straightforward SQL query against the database and use the resulting records.

In other words:

Without MCP:

• Some of the companies may not exist;
• Financial figures may be arbitrary;
• Company registration numbers may be invented.

With MCP:

• The model queries a database;
• It retrieves specific records;
• The database query produces a deterministic result.

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