RAISP03-BP04 Implement output filtering to detect and block hallucinations
Build filtering mechanisms that automatically detect and block factually incorrect outputs, hallucinations, and misleading information before they reach users. These filters act as a final check to catch inaccuracies that your core AI system might generate. Use both automated reasoning checks and fact verification systems to validate outputs against known facts and logical consistency before delivery.
Level of risk exposed if this best practice is not established: High
Implementation considerations
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Identify the specific types of veracity issues your system needs to filter based on your release criteria. Define what counts as hallucinations, factual errors, and misleading information for your use case.
For example, determine whether you need to catch mathematical errors, fabricated citations, invented statistics, or false historical claims.
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Design your filtering strategy by deciding which verification methods to use and how they will work together. Plan whether you need automated reasoning checks, external fact verification, confidence scoring, or human review processes. Create a filtering architecture that can handle your expected output volume and response time requirements.
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Implement your filtering mechanisms starting with automated reasoning checks that validate logical consistency, mathematical accuracy, and basic factual relationships. Add fact checking connections to reliable knowledge sources and databases. Build hallucination detection systems that can identify fabricated information patterns your system commonly generates.
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Test your complete filtering system using known examples of your system's typical errors and hallucinations. Measure how effectively your filters catch different types of inaccuracies without blocking too many accurate outputs. Adjust detection thresholds and add new filtering rules based on what you discover during testing.
Resources
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