{"id":23210,"date":"2026-07-03T13:38:09","date_gmt":"2026-07-03T10:38:09","guid":{"rendered":"https:\/\/grnet.gr\/?p=23210"},"modified":"2026-07-22T18:15:27","modified_gmt":"2026-07-22T15:15:27","slug":"pharos-ai-factory-training-series-course-9-rag-end-to-end-architecture-retrieval-generation-and-evaluation-on-july-7th-2026","status":"publish","type":"post","link":"https:\/\/grnet.gr\/en\/2026\/07\/03\/pharos-ai-factory-training-series-course-9-rag-end-to-end-architecture-retrieval-generation-and-evaluation-on-july-7th-2026\/","title":{"rendered":"PHAROS AI Factory Training Series &#8211; Course 9 &#8220;RAG End-to-End: Architecture, Retrieval, Generation and Evaluation&#8221;, on July 7th, 2026"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>PHAROS AI Factory<\/strong>&nbsp;announced the&nbsp;<strong>9th Course<\/strong>&nbsp;of its&nbsp;<strong>Training Series<\/strong>,&nbsp;<strong>with the title \u201c<\/strong><strong>RAG End-to-End: Architecture, Retrieval, Generation and Evaluation<\/strong><strong>\u201c<\/strong>, that successfully took place on July 7th, 2026.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presentation language<\/strong>: Greek<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Audience<\/strong>: This course was intended for Machine Learning Engineers, AI Engineers, Data Scientists, Academic Researchers, Language and Culture Experts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Learning Objectives<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Explain the core principles and architecture of Retrieval-Augmented Generation systems.<\/li>\n\n\n\n<li>Understand why RAG improves factuality, grounding, transparency and access to external knowledge.<\/li>\n\n\n\n<li>Describe the main RAG pipeline stages, from ingestion and preprocessing to retrieval and response generation.<\/li>\n\n\n\n<li>Identify design choices for chunking, embeddings, vector storage, retrieval, prompting and answer grounding.<\/li>\n\n\n\n<li>Evaluate retrieval quality, generation quality and end-to-end RAG behaviour.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Learning Outcomes:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A clear understanding of the main components and design paradigms of RAG systems.<\/li>\n\n\n\n<li>Practical familiarity with document preparation, chunking, embedding generation, vector indexing and similarity-based retrieval.<\/li>\n\n\n\n<li>Hands-on experience in constructing a working RAG pipeline using Python and contemporary tools.<\/li>\n\n\n\n<li>The ability to connect retrieved evidence with LLM-based answer generation in a grounded and transparent manner.<\/li>\n\n\n\n<li>Familiarity with evaluation approaches for retrieval, generation, faithfulness, groundedness and overall RAG performance.<\/li>\n\n\n\n<li>An understanding of how RAG can support Greek-language applications, including public-service information retrieval and conversational assistance.<\/li>\n\n\n\n<li>The skills to analyse, evaluate and improve RAG systems for real-world deployment<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The course\u2019s presentation material can be found&nbsp;<a href=\"https:\/\/events.grnet.gr\/event\/213\/timetable\/\">here<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The course\u2019s recordings can be found&nbsp;<a href=\"https:\/\/youtube.com\/playlist?list=PLCW4oYnblw0k&amp;si=xPdPfsUKxkHqhdJ9\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PHAROS AI Factory&nbsp;announced the&nbsp;9th Course&nbsp;of its&nbsp;Training Series,&nbsp;with the title \u201cRAG End-to-End: Architecture, Retrieval, Generation and Evaluation\u201c, that successfully took place on July 7th, 2026. Presentation language: Greek Audience: This course was intended for Machine Learning Engineers, AI Engineers, Data Scientists, Academic Researchers, Language and Culture Experts. Learning Objectives Learning Outcomes: The course\u2019s presentation material can [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":23211,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[294,355,490],"tags":[],"class_list":["post-23210","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-events-en","category-news-en","category-news_en-en"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23210","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/comments?post=23210"}],"version-history":[{"count":2,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23210\/revisions"}],"predecessor-version":[{"id":23399,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/posts\/23210\/revisions\/23399"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/media\/23211"}],"wp:attachment":[{"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/media?parent=23210"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/categories?post=23210"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/grnet.gr\/en\/wp-json\/wp\/v2\/tags?post=23210"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}