In Artificial Intelligence, hallucination and error are not the same thing.
Hallucination occurs because the model generates convincing answers, but without reliable data to support them.
Error, in turn, arises from traceable failures in rules, processes or data; and therefore, can be identified and corrected more objectively.
Differentiating the two is essential, because only in this way can we guarantee accuracy, reliability and governance.
Furthermore, this clarity helps to prioritize fixes, reduce rework, and then, make safer decisions.
In the full article, however, we show how to identify each of these problems in practice. and which strategies to adopt to reduce risks.
What is hallucination in AI?
Hallucination occurs when the model fills in gaps with assumptions, and often the text sounds correct but lacks a verifiable basis.
It is common, for example, to cite names, numbers or facts without a source.
This is because the model prioritizes fluidity instead of checking, and without connection to a database knowledge solid, then “plausible inventions” emerge.
Furthermore, it thrives in broad or ambiguous scenarios, as the system tries to please with convincing narratives; however, what it delivers is only apparent security.
The risk, therefore, is even greater in sensitive or regulated areas, because in these cases checking must be mandatory.
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What is error in AI
Error, unlike hallucination, has a traceable and repeatable cause.
It arises because there is noisy data, poorly defined rules, unstable pipelines, or even operational changes that impact the system.
For example: a dataset unbalanced distorts predictions; in addition, a poorly written rule generates a clear deviation; and in other cases, a failed connector affects only one flow.
This location of the problem, therefore, facilitates the correction plan.
However, resolving the error depends on a technical focus, as it requires adjusting data, rules, or processes to restore reliability.
Resolving the error depends on the technical focus: adjusting data, rules, or processes.
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How to differentiate in practice
- Hallucination: changes with each question, maintain tone trusting, but does not provide sources.
- Error: repeat the same defect in the same scenarios, pointing to a concrete origin.
Useful signs:
- Hallucination → exact numbers without data, non-existent entities, andexcess of confidence.
- Error → appears after releases, migrations or new datasets.
A/B testing helps:
- Hallucination improves with base of knowledge and external verification.
- Error corrected with specific adjustments in data and rules.
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Why hallucination happens
- Purpose of the model: to generate fluid text, not to check facts.
- Short context: loses references and invents shortcuts.
The solution: grounding with reliable data, larger context windows, and tighter risk controls.
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Why the error happens
- Business Changes: Rules and data stay outdated.
- Poor quality: inconsistent labels, samples unbalanced, old collections.
- Fragile operations: pipelines without versioning, few tests, limited logs.
Constant curation and documentation reduce errors and speed up corrections.
How to detect hallucination and error
- Checklist of facts: sources, evidence and risk.
- Repeat tests: reveal standards consistent (error) or variable (hallucination).
- Comparison with internal bases: contracts, policies, official documents.
- Monitoring continuous: absence of sources → hallucination; sudden drop in accuracy → error.
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Reduce risks with those who understand
THE Morpheus, from Matrix Go, unites specialized AI agents with trusted knowledge bases.
The platform uses verified sources, apply clear policies and validates sensitive sections.
The result is more accuracy and reliability, in a continuous and sustainable way.👉 Talk to Matrix Go and find out how Morpheus can transform your operation
