AI Development Best Practices: Handle Hallucinations

AI Development Best Practices: Handle Hallucinations

Dec 17, 2025 - 3 Min read

AI Makes Things Up. Plan for It.

Large language models hallucinate. They generate plausible-sounding information that’s completely fabricated. This isn’t a bug to be fixed—it’s a fundamental characteristic of how these models work.

Building reliable AI systems means designing for hallucinations, not wishing them away.

Why AI Hallucinates

Language models predict the most likely next token based on patterns in training data. They don’t “know” things—they generate statistically plausible text.

When asked something they don’t have good training data for, they generate plausible-sounding text anyway. That’s hallucination.

Common hallucination scenarios:

Specific facts (dates, numbers, names)
Citations and references (fake papers, non-existent URLs)
Recent events (after training cutoff)
Obscure topics (limited training data)
Confident extrapolation (extending patterns beyond evidence)

Strategies for Handling Hallucinations

Strategy 1: Retrieval-Augmented Generation (RAG)

Don’t ask AI to remember—give it the information.

Instead of: “What’s our refund policy?” Use: “Based on this document [policy.pdf], what’s our refund policy?”

RAG grounds responses in actual documents, dramatically reducing hallucinations about your specific data.

Strategy 2: Ask for Citations

Make the AI cite its sources:

“Answer this question and cite the specific section of the document where you found the information.”

If it can’t cite a source, it’s probably hallucinating.

Strategy 3: Constrain the Output

Give the AI explicit options:

“Based on this data, is the trend UP, DOWN, or FLAT? Choose only from these options.”

Constrained outputs are harder to hallucinate.

Strategy 4: Verification Loops

Have AI verify its own claims:

“You just stated X. Where in the provided documents is this supported? If you can’t find support, revise your answer.”

Self-verification catches many hallucinations.

Strategy 5: Lower Temperature

For factual tasks, reduce randomness:

Temperature 0: Most deterministic
Temperature 0.3-0.5: Balanced
Temperature 0.7+: More creative (and more hallucination risk)

Use low temperature for factual queries, higher for creative tasks.

Designing Hallucination-Resistant Systems

UI that encourages verification:

Show source citations alongside AI claims
Link to original documents
Flag low-confidence statements
Make it easy to verify

Workflows that include checks:

Human review for important outputs
Automated fact-checking where possible
Multiple AI queries to compare responses
Escalation for inconsistent outputs

Data architecture that supports RAG:

Well-organized document stores
Good embeddings for retrieval
Up-to-date source documents
Clear provenance tracking

Use Cases by Hallucination Tolerance

Low tolerance (be careful):

Legal documents
Medical information
Financial reporting
Compliance content

For these: RAG, citations, human review, constrained outputs.

Medium tolerance:

Customer support responses
Technical documentation
Business analysis

For these: RAG, sampling review, source linking.

Higher tolerance:

Creative brainstorming
Draft generation
Idea exploration

For these: Creativity matters more than precision.

When AI Says “I Don’t Know”

A well-calibrated AI should admit uncertainty:

“If you’re not confident about the answer based on the provided documents, say ‘I don’t have enough information to answer this confidently.’”

An AI that says “I don’t know” is more trustworthy than one that always answers confidently.

Monitoring for Hallucinations

Track hallucination rates in production:

User feedback: Did users flag incorrect information? Verification checks: Did automated checks find unsupported claims? Citation validity: Do cited sources actually support the claims? Consistency: Do repeated queries give consistent answers?

Hallucination monitoring is ongoing, not one-time.