RAG Chatbot for School District Staff Q&A
~75%
2–4 months
Not disclosed
School staff spent excessive time searching fragmented documents, different roles needed different information access, and outdated policies conflicted with newer ones.
PressW built a RAG-powered chatbot using parent-document retrieval and query expansion, with role-based access controls and hallucination guardrails that trigger deterministic fallbacks when no relevant document is found.
Districts upload documentation, which automated data pipelines convert into queryable data. The system was tested with query-expansion and parent-document techniques to bridge gaps between how staff phrase questions and how documents are written.
School districts and multi-role organizations that need staff to self-serve answers from large, fragmented policy libraries with role-appropriate access.
- District documentation uploaded by each district (source corpus)
- Automated data pipelines converting uploads into queryable data
- Role-based access control layer governing retrieval per staff role
- Multi-district deployment structure keeping each district's content separate
- District document upload to automated data pipeline to queryable index
- Parent-document retrieval and query expansion applied at query time
- Role-based access controls filtering retrieval by staff role
- Deterministic fallback path triggered when no relevant document is found
700 Pilot Users at Launch
1,600 Questions in Two Weeks
Guardrails Against Hallucination
Bryson Greenwood
Education Delta deployed a RAG chatbot over the district's own documentation. Districts upload their documents, automated pipelines convert them into queryable data, and parent-document retrieval with query expansion bridges the gap between how staff phrase questions and how policies are written. The pilot answered 1,600 questions in its first two weeks.
It runs on Claude with retrieval-augmented generation, using parent-document retrieval and query expansion, plus role-based access controls so different staff roles retrieve different information.
The pilot launched to 700 staff users and answered 1,600 questions in the first two weeks. Role-based retrieval with deterministic fallbacks kept answers grounded in real documents.
Time to results was under four weeks. The system was tested with query-expansion and parent-document techniques before the pilot launched to staff.
School districts and multi-role organizations that need staff to self-serve answers from large, fragmented policy libraries with role-appropriate access.
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