Hallucinations happen when a model produces unsupported or incorrect information. You cannot eliminate them completely, but you can reduce their frequency and impact.
Ground Answers in Sources
Use retrieval, citations, and source snippets for questions that depend on factual information.
Ask for Uncertainty
Tell the model to say when information is missing. A useful refusal is better than a confident guess.
Constrain Output Formats
For extraction and classification, schemas reduce room for invented structure and make validation easier.
Test Known Edge Cases
Create examples where the right answer is unknown, ambiguous, or outside the product scope. These tests reveal overconfidence quickly.
The best product design treats the model as powerful but fallible, then builds verification around the moments where accuracy matters most.
Frequently Asked Questions
No. They can be reduced with grounding, validation, and careful product design.
They help, but the system must verify that citations actually support the answer.
Add retrieval with source citations and require the model to admit when evidence is missing.