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Steven Jannette
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# Source Sentinel - 2025 @sjDev - LICENSE: MIT
# Citation Sentinel - 2025 @sjDev - LICENSE: MIT
![SOURCE Sentinel UI showing the source sidebar, natural language answer with citations to the source, and groundedness badge](demo.jpg)
![Citation 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.
**Source Sentinel's leverages multiple Large Language Models for analyzing and conveniently working with large data corpora, when query-result accuracy is of the highest value. Examples include complex litigation (i.e. Patent/IP), technical medical data exploration (reviewing health records, test results, or studies to find hidden patterns, trends, and answers), advanced LLM development and refinement.**
**Citation Sentinel leverages multiple Large Language Models for analyzing and conveniently working with large data corpora, when query-result accuracy is of the highest value. Examples include complex litigation (i.e. Patent/IP), technical medical data exploration (reviewing health records, test results, or studies to find hidden patterns, trends, and answers), 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.
Citation 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.
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.
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# Reliability and Understandability
Source Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space. This yields a "groundedness" score.
Citation Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space. This yields a "groundedness" score.
Groundedness metrics quantify how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated, inaccurate or stale (pulled from the model's training data).