To detect and correct factual errors or hallucinations in outputs from Bedrock’s generative models, implement a layered validation approach in your application workflow. Start by integrating explicit fact-checking mechanisms. For example, use APIs like Google Fact Check Tools or custom validation logic that cross-references outputs against trusted databases or knowledge graphs. You can also extract key entities, dates, or claims from the generated text and validate them against structured data sources (e.g., Wikidata, internal databases). For instance, if the model generates a statement like "Einstein discovered quantum entanglement," your workflow could flag "quantum entanglement" and check against a physics database to confirm the correct attribution (e.g., validating it was actually Schrödinger or others). This step adds a verification layer before presenting results to users.
Next, incorporate confidence scoring and human review. Many generative models provide confidence scores or token-level probabilities for their outputs. Use these scores to identify low-confidence assertions and route them for further inspection. For example, if a generated medical diagnosis has low confidence in specific symptom-cause relationships, flag it for review by a domain expert. Additionally, design your application to allow seamless human intervention, such as a dashboard where moderators can correct errors or append disclaimers. In workflows where real-time correction isn’t feasible, consider appending caveats like "This information requires verification" to outputs that your fact-checking layer deems uncertain.
Finally, iterate on prompts and use post-processing rules. Refine your model inputs with explicit instructions to reduce hallucinations, such as "Only include facts verified by [source]" or "If uncertain, state 'No data available.'" Pair this with post-processing scripts that detect common hallucination patterns, like unsupported superlatives ("the first ever") or vague references ("studies show"). For example, a regex-based filter could flag phrases like "according to experts" without named sources and either remove them or trigger a re-generation. Continuously log errors and retrain the model with corrected data or fine-tune it on domain-specific datasets to improve accuracy over time. By combining automated checks, human oversight, and iterative improvements, you can systematically mitigate errors while maintaining scalable workflows.