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UID:13d2ea56-fbdc-417a-ac5d-7df8d8441efd@support.access-ci.org
DTSTAMP:20260908T131151Z
DTSTART:20260914T170000Z
DTEND:20260914T180000Z
SUMMARY:CU-RMACC Webinar - Principles for Effective, Repeatable LLM-Assiste
 d Workflows
DESCRIPTION:Emergent Generative AI and LLM technologies can be effective to
 ols for individual tasks. Using them in a repeatable, reproducible manner 
 that is efficient, without sacrificing critical thinking, has proven more 
 elusive. This presentation will outline principles that reduce time and ef
 fort without removing the critical 'human-in-the-loop'. These fundamentals
  can be adapted to any modern LLM platform, and represent an alternative a
 pproach to leveraging LLMs.Presenter: Ward Fisher is a Computer Scientist
  with a background in Machine Learning. For the last 14 years, he has work
 ed as a Research Software Engineer at NSF Unidata, a community program man
 aged by the University Corporation for Atmospheric Research (UCAR), the ma
 naging entity for the National Center for Atmospheric Research (NCAR).
URL:https://support.access-ci.org/events/9280
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