Hallucination: definition, causes and safeguards
Why the Term Matters
Whoever puts an AI assistant in front of customers or into internal processes is liable for what it says. An invented price or a false legal statement can get expensive. So the key question is not whether a model sounds clever but whether its answers can be backed up. A working prototype proves its value right here, because it exposes the hallucination risk on real cases before the build.
When It Is Relevant
- The assistant gives information that people base decisions on.
- Expert knowledge, prices or contract data flow into the answers.
- Answers need to be provable, not just plausible.
- A wrong statement would carry legal or financial consequences.
When Not
For purely creative tasks such as idea collection, draft text or brainstorming, a degree of inventiveness is welcome and no danger. If a human checks every output before it takes effect, the risk drops too. It only becomes critical where the answer goes out unchecked or triggers an action.
Industries That Use It
- Insurance: statements about tariffs and coverage must be provable, otherwise misadvice and liability follow.
- Healthcare: invented dosages or study figures are dangerous, so every statement needs a checked source.
- Public administration: citizen information must match current law, free interpretation is not acceptable.
- Financial services: numbers and conditions must not be guessed, because binding decisions follow from them.
Typical Use Cases
- A support assistant quotes only from approved documents and flags when it finds nothing.
- A research assistant links every statement to the passage it used.
- An internal knowledge bot answers with I do not know instead of guessing.
- A quote generator pulls prices from a checked data source rather than model memory.
- A review loop lets a second model check the first answer for evidence.
Decision Guide
| Signal | Safeguards needed | Alternative |
|---|---|---|
| Answers support decisions | add retrieval plus source binding | free model only with a human check |
| Purely creative use | light safeguard is enough | standard model without extras |
| High liability on error | strict review loop and logging | do not automate the case at all |
| Knowledge changes often | connect to a live data source | no static model answer |
Example
An energy provider wants to automate tariff information. In the workshop a prototype is built that answers only from the approved tariff catalogue and links every number to its source. On real queries it becomes clear where an unconnected model invented and where source binding holds. This describes the method, not an asserted client metric.
Service Connection
The right entry is the AI workshop, where the hallucination risk is tested on real cases, then AI development and AI implementation.
Sources
This framing describes common practice for handling hallucinations in language models. Technical and legal details depend on the concrete model, the data and the use and should be checked there.