An AI “hallucination” is when a language model produces a confident, plausible statement that’s false or not supported by the evidence it was given. Hallucinations happen because models generate likely text (not verified facts) and can be incentivized to guess instead of abstain when uncertain.
Try CustomGPT’s 7-day free trial to enable “My Data Only” and Anti-Hallucination settings.
TL;DR
AI hallucinations occur when models generate confident, plausible, but false or unsupported statements, often due to predictive guessing or missing evidence. Reducing them requires treating the issue as a system problem: enforce grounding in approved data, require citations for every claim, and enable safe abstention when sources are insufficient.
Pick one high-risk topic, enforce strict “sources or abstain” rules, and track groundedness weekly until the failure rate drops.
What Are AI Hallucinations?
An AI hallucination is an output that sounds correct but is false, misleading, or unsupported, especially dangerous when delivered confidently. OpenAI describes hallucinations as plausible but false statements generated by language models.
Related Terms
These terms describe common ways hallucinations appear, from outright fabrication to answers that are outdated or unsupported by sources.
- Fabrication / Confabulation: The model invents facts.
- Source-Unsupported Answer (Extrinsic Hallucination): The answer isn’t supported by the provided documents/sources.
- Stale/Temporal Error: The model presents outdated information as current (often confused with “hallucination”).
Quick Checklist for Chatbot Owners
Use this checklist to quickly assess whether your chatbot has the necessary safeguards to prevent confident, unsupported answers.
- Does every factual answer have a supporting source?
- Is the bot allowed to answer only from approved docs (or does it freewheel)?
- Do you have a clear refusal / “I don’t know” path?
- Are temperature/decoding settings tuned for determinism in support flows?
- Are you measuring groundedness + citation coverage continuously?
Why Hallucinations Happen
Hallucinations are usually a system outcome, not a single bug. Common causes cluster into two layers:
Model-Level Causes
These causes stem from how language models are trained to predict text rather than verify factual accuracy.
- Text prediction, not fact verification: The model is optimized to produce likely continuations, which can be fluent even when wrong.
- Guessing can be rewarded: OpenAI argues standard training and evaluation procedures can reward answering over acknowledging uncertainty.
System-Level Causes
These issues arise from missing, weak, or incorrect context provided to the model at runtime.
- Missing evidence in context: If the model can’t “see” the needed policy/doc snippet, it may fill the gap with a plausible guess.
- Weak retrieval (RAG failure modes): Retrieval can return irrelevant chunks, stale docs, or partial context, leading to confident-but-unsupported answers.
- Data quality issues: Biased/inaccurate training or grounding data increases error rates (IBM highlights training data bias/inaccuracy as a factor).
What Hallucinations Look Like in Real Chatbots
High-signal patterns you can detect in production:
- Invented or mismatched citations: The bot references sources that don’t support the claim (or don’t exist).
- Confident product inaccuracies: “Feature X supports Y” when docs don’t say that.
- Policy/date mistakes: Eligibility windows, renewal dates, refund rules stated incorrectly.
- Over-precise numbers with no basis: “98.2% improvement” without a measurable source.
Why it’s risky: the harm isn’t just that errors occur, it’s that confidence makes them easy to trust and hard to catch at scale.
How to Reduce AI Hallucinations in Chatbots
You can’t guarantee “never,” but you can reduce hallucinations by increasing groundedness, enforcing abstention, and improving retrieval + evaluation.
1) Ground the Bot in Approved Evidence
Retrieval-Augmented Generation (RAG) retrieves relevant passages first, then uses them as the basis for the answer. The canonical RAG paper motivates retrieval + provenance to improve factual behavior on knowledge-intensive tasks.
Minimum viable grounding rules:
- Define a ground-truth corpus: policies, docs, help center, runbooks (and keep them current).
- Retrieve → answer: don’t “answer first and hope.”
- Prefer source-supported outputs: if the docs don’t contain the answer, the bot should not invent it.
2) Require Citations
For user-facing support bots, make “show sources” the default:
- Tie key assertions to specific retrieved passages
- Treat “no citation available” as a failure state for factual questions
3) Enforce “Abstain or Clarify” When Evidence Is Weak
Add explicit fallback behavior:
- Ask a clarifying question when the user’s question is underspecified
- Say “I don’t know” (or “Insufficient data”) when the docs don’t support an answer
4) Reduce Randomness for Support Use Cases
If your bot’s job is correctness over creativity:
- Use lower temperature / more deterministic settings for factual, repetitive support flows (Microsoft explicitly calls out temperature control).
5) Evaluate and Monitor Groundedness
Operationalize hallucination reduction:
- Maintain a regression set of known tricky and out-of-scope questions
- Track: citation coverage, groundedness, and abstention rate (when appropriate)
- Review top failure conversations weekly; treat each hallucination as a backlog item: fix data → fix retrieval → fix prompting → add guardrail
Example: Fixing a Refund-Policy Bot That Confidently Answers Wrong
This example shows how hallucinations can be resolved by fixing data access, retrieval, and refusal behavior rather than the model itself.
Scenario: Users ask refund questions; the bot answers confidently with no supporting citation.
Before (hallucination pattern):
User: “Can I get a refund after 45 days?”
Bot: “Yes, refunds are available for up to 60 days.” (No citation.)
Fix (system changes):
- Add the actual refund policy text to the approved knowledge base.
- Enable RAG + citations for policy questions.
- Add a rule: If the refund window isn’t in the retrieved policy snippet, abstain and escalate.
After (grounded output hypothetical example):
Bot: “I can’t confirm a refund window from the available policy text.”
Bot: “If you share your order date (or the policy link), I can check again.”
How This Maps to CustomGPT.ai
If your requirement is “answers only from our docs,” CustomGPT’s docs describe three practical levers:
- Keep Generate Responses From set to My Data Only (restrict answers to uploaded content).
- Keep Anti-Hallucination enabled (Security tab).
- Enable citations and tune the fallback message (Citations tab: Enable Show Citations + customize “I don’t know”).
Conclusion
Hallucinations are best treated as a design and operations problem: ensure the bot has access to the right evidence, require citations, and make abstention a safe default when sources don’t support an answer.
Now what: pick one high-risk support topic (refunds, billing, eligibility), enforce “sources or abstain,” and track groundedness weekly until the failure rate is boring with the 7-day free trial.
FAQ
Is An “Unsupported-By-Docs” Answer Always A Hallucination?
Not always. In many support bots, the failure is missing evidence, not pure fabrication: retrieval returned nothing relevant, or the source corpus doesn’t contain the answer. That’s why “show sources or abstain” is effective, your system can refuse safely when grounding is absent.
Does Lowering Temperature Actually Reduce Hallucinations?
Lower temperature often makes outputs more deterministic and less creative, which can reduce variation and “storytelling” in support flows. But it won’t fix missing evidence by itself, if the model can’t access the right policy snippet, it can still be confidently wrong. Treat temperature as a tuning knob, not the foundation.
Where Do I Configure “My Data Only,” Citations, And Anti-Hallucination In CustomGPT?
CustomGPT documents these controls in Agent Settings: Citations tab (enable citations + customize “I don’t know”), Security tab (Anti-Hallucination), and Intelligence tab (Generate Responses From). The “defense” doc also notes the default is “My Data Only” and explains the tradeoff if you enable broader LLM knowledge.
Is RAG Enough, Or Do I Still Need Monitoring?
RAG helps by restricting answers to retrieved sources, but you still need monitoring because retrieval can fail (wrong chunk, stale doc, missing policy). A practical baseline is: measure groundedness/citation coverage, keep an eval set of risky queries, and add a refusal/escalation path when evidence is weak.