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SUMMARY:RAG evaluation: retrieval quality\, faithfulness\, groundedness an
 d RAGAS/DeepEval concepts
DTSTART;VALUE=DATE-TIME:20260707T114000Z
DTEND;VALUE=DATE-TIME:20260707T120000Z
DTSTAMP;VALUE=DATE-TIME:20260703T161445Z
UID:indico-contribution-1156@events.grnet.gr
DESCRIPTION:Speakers: George  Drosatos (ATHENA RC)\, Sotiris  Gyftopoulos 
 (ATHENA RC)\nThis evaluation session explains how to assess RAG systems be
 yond a single final answer score. Participants will learn why evaluation m
 ust decompose the pipeline into retrieval\, generation\, citation quality 
 and end-to-end behaviour. The session introduces retrieval metrics such as
  Precision@k\, Recall@k\, Mean Reciprocal Rank and NDCG\, showing how they
  describe the evidence made available to the model. It then discusses answ
 er-level criteria\, including correctness\, faithfulness\, groundedness\, 
 citation quality\, completeness and relevance. Participants will also be i
 ntroduced to RAGAS and DeepEval concepts for automated RAG evaluation\, re
 gression testing and structured comparison of system variants. The emphasi
 s is diagnostic: metrics should identify failure modes and guide concrete 
 improvements\, such as better chunking\, query handling\, filtering\, rera
 nking or prompt constraints. By the end\, participants will understand how
  to evaluate and iterate RAG systems systematically. This prepares them to
  maintain RAG quality as data\, prompts and models evolve in operational s
 ettings\, after deployment too.\n\nhttps://events.grnet.gr/event/213/contr
 ibutions/1156/
LOCATION:
URL:https://events.grnet.gr/event/213/contributions/1156/
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