Section 3: What hallucinations look like
Module 7 — Data Safety & Common Mistakes
Section 3: What Hallucinations Look Like
Purpose of This Section
This section explains what AI hallucinations are, why they occur, and why they are one of the most common and dangerous failure modes when using AI at work.
Hallucinations are not rare. They are a known limitation.
The Core Idea
A hallucination occurs when an AI produces information that sounds correct but is factually wrong.
AI systems generate responses based on patterns, not verification. As a result, they may confidently invent details, references, or explanations that do not exist.
Confidence does not equal accuracy.
Why Hallucinations Are Dangerous
Hallucinations are difficult to detect because they are often:
- fluent and well-structured
- logically explained
- supported by plausible-sounding references
They do not usually appear obviously broken. They appear almost right, which makes them easy to trust and repeat.
Common Examples
Hallucinations often take forms such as:
- incorrect dates or timelines
- laws or policies that were never enacted
- fabricated studies or citations
- features or capabilities that do not exist
- misattributed quotes or actions
These errors can propagate quickly if not caught.
When Hallucinations Are More Likely
Hallucinations are more likely when:
- information is recent or rapidly changing
- topics are niche or poorly documented
- prompts request definitive answers without sources
- users push beyond established knowledge
Speed and ambiguity increase risk.
How to Use AI Safely
AI should be used as an orientation and exploration tool, not a final authority.
When accuracy matters:
- verify dates, names, and figures
- check original sources
- confirm claims independently
Human judgment is required to catch errors.
Common Failure Mode
A common mistake is assuming that fluent, confident responses are reliable by default.
Another failure mode is repeating AI-generated information without verification, allowing errors to scale across teams or documents.
Errors become more costly as they spread.
The Conjugo Rule
If it matters, verify it.
AI can help you think faster, but it cannot guarantee correctness.
Best Practices
Managing hallucination risk works best when:
- critical information is cross-checked
- sources are explicitly requested
- outputs are reviewed skeptically
- verification is built into workflows
Accuracy requires intention.
Section Takeaway
- Hallucinations are confident errors
- They often sound polished and complete
- Risk increases with novelty and ambiguity
- Verification remains human responsibility
Hallucinations are a limitation to manage, not a flaw to ignore.
This concludes Section 3.