RAISP02-BP08 Consider core AI system designs that improve factual accuracy

Design and implement knowledge grounding strategy using RAG architecture or parametric knowledge, verifying clear version control of knowledge bases and rigorous source validation process.

Create training objectives that explicitly reward factual accuracy and penalize hallucinations, incorporating self-critique methods and negative examples while using tools like Constitutional AI or RLHF.

Build uncertainty quantification into model behavior through calibrated confidence scores and explicit knowledge boundaries, training the system to acknowledge limitations rather than generate plausible but unverified responses.

Establish continuous feedback loops to identify and correct factual errors, implementing regular validation cycles against authoritative sources and domain-specific accuracy metrics.

Use output validation to check that your system's responses are accurate and relevant. This is used to detect when your system makes up information by comparing responses against trusted sources and using logical checks to verify facts are correct. For example, Amazon Bedrock Guardrails provide capabilities for detecting hallucinations in model responses using contextual grounding checks. Automated Reasoning checks in Amazon Bedrock Guardrails assists to block factual errors from hallucinations using logically accurate and verifiable reasoning that explains why responses are correct. Automated Reasoning assists to mitigate hallucinations using sound mathematical techniques to validate/correct, and logically explain the information generated leading to outputs that align with known facts and are not based on fabricated or inconsistent data.