Evidence-first design ties every AI claim to stored, auditable sources

The DEV Community article outlines a method for generating AI responses that attach each claim to verifiable evidence, addressing hallucinated figures and untraceable recommendations. It recommends persisting citation metadata such as document ID, version, timestamp and excerpt alongside each response. Numeric values that change over time, such as regulatory limits or interest rates, should instead come from a deterministic live-value API rather than static documents. The piece also includes a component breakdown covering retrieval, citation management, validation, the UI, and audit logging, along with sample Python functions for building and checking evidence records. It notes that retrieval-augmented generation alone does not guarantee factual accuracy, so evidence must be validated separately.