Merge pull request 'Minor cleanup' (#7) from FEAT-minor-cleanup into master

Reviewed-on: #7

README.md

@@ -1,13 +1,13 @@
-# 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 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.**
+**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.
 
@@ -15,7 +15,7 @@ ALSO NOTE IN DEMO: clickable inline citations (in blue) that take user to source
 # 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).