ExtensionBot: A Domain Specific Gen AI Tool for the Cooperative Extension Service
summary
A domain-specific conversational agent trains a retrieval-augmented LLM on roughly 300,000 resources from land‑grant institutions, ingesting JSON dumps or crawling public HTML/PDFs with Python and Beautiful Soup, embedding chunks and storing them in cudan vector DB for similarity search; front end uses Rasa widget and a full‑page chat, back end is a RAG pipeline hosted on Google Cloud. It runs in production at extension.org and on several state sites, returns cited fact‑sheets, supports state localization, and flags unknowns; challenges include PDF parsing, synonyms, updating crawls and cost.
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chapters (23)
- 0:00Introduction and speaker setup
- 1:08What is Cooperative Extension Service
- 2:15Augmented intelligence vs AI
- 2:58What is ExtensionBot
- 3:20ExtensionBot vs ChatGPT comparison
- 6:06Technology behind ExtensionBot
- 7:31Data sources and formats
- 8:38Gathering data for training
- 10:48JSON data structure example
- 11:52Data set size and scalability
- 13:18Data ingestion process
- 14:24Implementation and user interface
- 16:50Configuration and localization
- 18:16Challenges in development
- 22:51Cost and model efficiency
- 23:53Q&A session introduction
- 24:15Marketing and user adoption
- 26:21Feedback and safety concerns
- 28:52Handling unknown queries
- 29:13Data storage and security
- 31:00Knowledge graphs and future tech
- 32:03Testing and evaluation challenges
- 33:05Marketing to farmers and agents
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