Data@Urban
Explore the code, data, products, and processes that bring Urban Institute research to life.
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A person’s financial well-being is nuanced, encompassing many different metrics and situations. A single dataset rarely paints a complete picture of people’s financial lives. Therefore, building a holistic understanding of financial well-being often requires linking data from several disparate sources.
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Building an Agentic AI Tool to Support Our Upward Mobility Initiative Dashboard

- We're building an agentic AI tool for our Upward Mobility Initiative Dashboard, using a custom lookup tool to reliably match place names to the right data, even in tricky cases like ambiguous city and county names. This agentic approach outperformed our earlier chatbot, handling edge cases smoothly without needing a smarter model, just better structure. We're excited to build on this as we move toward a single front door for AI-supported mobility technical assistance.
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How We Built an AI Evaluation Framework with Experts in the Loop

- We built an AI evaluation framework with domain experts in the loop to test whether our Upward Mobility Initiative knowledge base gave accurate, well-sourced answers, then used their feedback to fix retrieval and citation issues across two rounds of testing. We found the model performed well on straightforward questions but struggled with complex, multidocument ones, and that automated metrics alone couldn't replace expert judgment.
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What It Takes to Make Research and Policy Knowledge AI Ready

- We built an AI-ready knowledge base for Urban's Upward Mobility Initiative by combining careful document curation, preprocessing, and metadata tagging, and found that these steps meaningfully improved how well AI tools could ground answers in trusted research and data.
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Rapid Prototyping with Agentic AI to Make Better Technical Decisions, Faster

- Two recent projects show how agentic tools like Claude Code let us prototype and test technical approaches in minutes, ruling out dead ends before committing p…Two recent projects show how agentic tools like Claude Code let us prototype and test technical approaches in minutes, ruling out dead ends before committing project time and budget, and take on more ambitious work with confidence.
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Introducing AI@Urban: Practical, Evidence-Based Learning on AI in Public Policy

- We are introducing AI@Urban, a new series within the Urban Institute's Data@Urban platform dedicated to sharing practical, evidence-based learning at the intersection of AI and public policy. Through quick, digestible posts, we'll cover what we're learning as we incorporate AI into our policy work and support government and nonprofit partners in using it responsibly.