What Is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an approach that allows AI systems to retrieve relevant information from external sources when generating responses. Instead of relying only on model training, RAG incorporates documents, webpages, and structured data at the time a question is asked.
This improves access to information and helps ground AI-generated answers. However, retrieval alone does not guarantee that information is attributed to the correct authority, or interpreted within the appropriate jurisdiction.
Retrieval Only Helps AI
Find Information.
It Does Not Define Authority.
Retrieval-Augmented Generation, or RAG, improves how AI systems locate and assemble information from available sources. But retrieval alone does not guarantee that public information is attributed to the correct government authority, timestamped clearly, or interpreted within the right jurisdiction.
What RAG Does
RAG helps AI systems retrieve relevant documents, webpages, or records when responding to a question. It can improve grounding by connecting AI-generated answers to available source material instead of relying only on model memory.
For public information, this matters because residents increasingly ask AI systems about cities, departments, public services, alerts, closures, and advisories before visiting official websites.
Finds Relevant Material
RAG improves the ability of AI systems to locate documents, pages, and records that appear relevant to a user’s question.
Grounds AI Responses
Retrieval can reduce unsupported responses by anchoring answers to available source material rather than relying only on generated text.
Operates Downstream
RAG selects from information that already exists. It does not, by itself, define who issued that information, when it was issued, or where it applies.
Where Retrieval Stops
Retrieval does not automatically resolve attribution. If government information is spread across webpages, PDFs, social posts, agendas, notices, and department pages, AI systems may retrieve relevant material while still lacking clear authority signals.
Attribution Can Remain Unclear
Retrieved information may still be difficult to attribute when multiple departments, agencies, or jurisdictions publish related updates.
Timing Can Drift
Without consistent timestamps, AI systems may retrieve older material alongside newer updates without a clear signal of which source is current.
Jurisdiction Can Collapse
City, county, regional, and agency-level information can appear similar to AI systems unless jurisdiction is defined explicitly and consistently.
AI Citation Registries Address
the Upstream Layer.
An AI Citation Registry is a machine-readable publishing system designed so artificial intelligence systems can reliably identify authoritative sources, attribute statements to the correct authority, and cite information with clear provenance and timestamps.
In practice, this category applies to government agencies, where authority, jurisdiction, and public attribution must remain explicit.
Retrieval vs Attribution
RAG helps answer the question: what information is relevant? AI Citation Registries help answer a different question: who is the authoritative source, when was the information issued, and what jurisdiction does it apply to?
RAG Retrieves Information
Retrieval systems select and assemble information from available sources. Their value is access: finding relevant material at the moment a question is asked.
Registries Define Authority
AI Citation Registries structure attribution, timing, and jurisdiction before retrieval occurs, giving AI systems clearer source signals to work with.