{"product_id":"9783032395313","title":"Verified AI Systems for Technical Reasoning Evidence, Retrieval, Uncertainty, and Evaluation for Reference-Aware Assistants","description":"\u003ch1\u003eVerified AI Systems for Technical Reasoning\u003c\/h1\u003e\u003ch2\u003eEvidence, Retrieval, Uncertainty, and Evaluation for Reference-Aware Assistants\u003c\/h2\u003e\u003ch3\u003eJose Unpingco\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eTechnology \u0026amp; Engineering \/ Telecommunications\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eThis book provides a practical and rigorous guide to building AI systems whose technical answers can be inspected, checked, and improved. It explains why fluent language generation and retrieval-augmented generation are not enough by themselves and then develops the evidence pipeline needed for trustworthy technical reasoning. Readers learn to treat an AI assistant as a reference-aware system rather than as a model alone. Each chapter turns reliability into concrete questions: What was retrieved? Which claim does each citation support? What assumptions were used? What can be recomputed or tested? What remains uncertain? When should the system abstain? The book is intended for machine learning engineers, data scientists, software engineers, applied researchers, technical managers, and advanced students who need to design or evaluate AI assistants for high-value technical work.\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eProvides a practical guide to building AI systems whose technical answers can be inspected, checked, and improved\u003c\/li\u003e\n\u003cli\u003eIncludes evidence-first RAG methodology connecting retrieval design choices to citation fidelity and answer reliability\u003c\/li\u003e\n\u003cli\u003ePresents claim-level verification with uncertainty-aware response policies for trustworthy technical reasoning\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eDr. José Unpingco completed his Ph.D. in electrical engineering from the University of California, San Diego (UCSD), in 1998, where his thesis focused on developing a novel neural network for processing multipath acoustical signals for the US Navy. Over a career spanning more than two decades, he has worked extensively across industry and government sectors as AI Leader, Machine Learning Engineer, Consultant, and Educator.\u003c\/p\u003e\n\u003cp\u003eAs Director of Signal and Image Processing (SIP) for OSC and HPTi, he directed large-scale computational science activities and spearheaded the DoD-wide technology transfer and adoption of scientific Python across nationwide research laboratories.\u003c\/p\u003e\n\u003cp\u003eDr. Unpingco spent over eight years at West Health, a non-profit medical research organization. He initially served as Senior Director for Data Science and Machine Learning before being promoted to Vice President of Data Science and Machine Learning.\u003c\/p\u003e\n\u003cp\u003eDr. Unpingco is currently Senior Staff AI\/ML Engineer at ServiceNow.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e01 February 2027\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032395313\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":52370143314060,"sku":"9783032395313","price":89.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032395313","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}