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Steven Jannette
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# Source Sentinel - 2023 @sjDev - LICENSE: MIT # Source Sentinel - 2023 @sjDev - LICENSE: MIT
![SOURCE Sentinel UI showing the source sidebar, chat answer with citations to the source, and groundedness badge](demo.jpg) ![SOURCE Sentinel UI showing the source sidebar, natural language answer with citations to the source, and groundedness badge](demo.jpg)
Image one: source-grounded research assistant for applied use of LLMs.
Image one: source-grounded research assistant that empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses. The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals. **Source Sentinel's value proposition: analyzing and conveniently working with large data corpora, when query-result accuracy is of the highest value. Examples include technical medical data exploration (reviewing health records, test results, or studies to find hidden patterns, trends, and answers), complex litigation (i.e. Patent/IP), advanced LLM development and refinement.**
Source Sentinel empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses. The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals.
Image Two: demo co clickable inline citations that take user to source-grounded research basis/knowledge corpus supporting query response, with rated “groundedness” score. ALSO NOTE IN DEMO: clickable inline citations (in blue) that take user to source-grounded research basis/knowledge corpus supporting query response, with rated “groundedness” score.
# Reliability and Understandability # Reliability and Understandability