AI hallucination is the phenomenon of AI answering confidently but incorrectly or fabricating information that is not in the data - the biggest risk when using AI for customer service, but it can be prevented with procedures. Zenify recorded a 35% reduction in processing time (approved).
TL;DR
- AI "makes up" when they answer confidently but wrongly.
- Occurs when data is missing, prompt is ambiguous.
- Reduced by: range limit + private data.
- Sensitive answers require approval.
- Continuous measurement for early detection.
What is AI hallucination?
AI hallucination is when the model generates information that is not real or not in the data but confidently presents it as fact. For example, AI claims a "100% all cases" refund policy — when the actual policy is different.
Because AI learns to predict from language (see What is LLM), it can "match" answers fluently without meaning.
Vì sao AI bịa?
Cause | Explanation
Missing private data | There is no information about your business
Vague prompt | The allowed scope is unclear
Questions beyond knowledge | Ask about something you've never learned before
Pressure to answer | There is no "I don't know"
Dirty data | Learning from the wrong source
5 ways to avoid fake AI
1. Individual feed grade (RAG)
Let AI read real policies, products, and processes (see RAG).
Source-based, citation-able answer.
2. Limit scope
Only let AI answer in the prepared topic.
Out of scope → reply "I will transfer to support staff".
3. Ask to say "don't know"
Set instructions: if you lack information, ask again, don't guess.
Kết hợp với Prompt engineering.
4. Sensitive sentence reviewer
Commitment, compensation, pricing policy — who checks before sending (see AI governance).
5. Measurement and monitoring
Take sample answers and score correct or incorrect answers.
Sửa kịp thời, tinh chỉnh prompt/dữ liệu.
Signs AI is making things up
Unusually confident with numbers and specific commitments.
Do not cite source when asked "based on what".
Contradictions between answers.
I don't know but still answer instead of asking again.