As large language models and AI chat tools become one of the fastest-growing ways people get information, the accuracy of what those tools say about government and public life depends on the data behind them. For decades, government statistical agencies have served as the backbone of authoritative information for the public and policymakers alike. But most of that data was built for a pre-AI world, and gaps in standardization, availability, and consistency limit how well AI systems can incorporate it and provide accurate facts.

We see the strongest path forward as a systems-based one: pairing large language models with information-retrieval methods — like retrieval-augmented generation, agents, or web search — and holding the results to a clear set of accuracy evaluations. Four criteria describe what it takes to get government data to that standard: accessible, understandable, accurate, and open.