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SUMMARY:Why RAG? Hallucinations\, knowledge cut-offs and grounded generati
 on
DTSTART;VALUE=DATE-TIME:20260707T080000Z
DTEND;VALUE=DATE-TIME:20260707T081500Z
DTSTAMP;VALUE=DATE-TIME:20260703T180746Z
UID:indico-contribution-1139@events.grnet.gr
DESCRIPTION:Speakers: George  Drosatos (ATHENA RC)\nThis opening session e
 xplains why Retrieval-Augmented Generation is needed when Large Language M
 odels must answer with factual\, current and verifiable information. It in
 troduces the main limitations of standalone LLMs\, including static traini
 ng knowledge\, knowledge cut-offs\, hallucinations and weak handling of sp
 ecialised domain terminology. Using Greek public-service information as a 
 motivating example\, the session shows how RAG changes the task from gener
 ating from model memory to answering from retrieved evidence. Participants
  will see how an external knowledge source can support more transparent\, 
 context-aware responses\, especially in domains where users need reliable 
 guidance\, citations and clear caveats. The session establishes the centra
 l concepts used throughout the webinar: user query\, retrieval\, evidence\
 , grounded answer and source attribution. By the end\, participants will u
 nderstand the practical motivation for RAG and why retrieval quality matte
 rs before any answer is generated by the model. It clearly frames RAG as a
  reliability pattern for specialised knowledge-intensive applications.\n\n
 https://events.grnet.gr/event/213/contributions/1139/
LOCATION:
URL:https://events.grnet.gr/event/213/contributions/1139/
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