Retrieval quality, not the model, decides whether an AI assistant is useful
Chunking, metadata, permission filtering, and evaluation decide whether an agency assistant is useful, and the model is the replaceable part.
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AI posts
HQT blog posts on ai for public agency technology teams.
Small open models are good enough for many agency tasks
The largest model is not always the right one. For classification, extraction, and grounded answering, smaller models running in the agency's own environment often do the job.
An acceptable use policy for generative AI should govern data, not tools
What a generative AI acceptable use policy must cover, what it should leave alone, and how to keep it from going stale.
Your vendor's new AI feature is a new system, not an update
Vendors are adding AI to platforms agencies already license. Here is what to test first, and what to leave switched off.
Using AI to triage service requests without losing the human
How classification models can route 311-style requests, and the guardrails that keep residents from falling through.
Running language models inside the agency's cloud boundary
For sensitive data, the model should come to the data. Here is what that looks like in practice.
What we learned building an assistant that answers questions from a municipal code
A retrieval-augmented assistant over a published city code, built as an internal exercise. The hard parts were not the model.