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PRODID:IEEE vTools.Events//EN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
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BEGIN:VEVENT
DTSTAMP:20261008T110541Z
UID:E2382A66-9344-468E-9609-BBA1D900C753
DTSTART;TZID=Asia/Kolkata:20261002T190000
DTEND;TZID=Asia/Kolkata:20261002T200000
DESCRIPTION:Prompt engineering sits at the intersection of human intent and
  machine execution\, transforming raw large language models into targeted\
 , reliable tools. Grounded in core model mechanics: such as token processi
 ng\, probability distributions\, temperature\, and top-p sampling\, effect
 ive prompt design helps direct model variability\, align output with human
  values\, and mitigate hallucinations. The discipline encompasses structur
 al prompt construction using clear context\, delimiters\, and desired form
 atting\, alongside strategies like role-playing\, few-shot examples\, and 
 step-by-step Chain of Thought reasoning. For complex domain requirements\,
  prompt strategies extend to Retrieval-Augmented Generation (RAG) and fine
 -tuning. Ultimately\, building production-ready prompts relies on software
  engineering rigor\, including input sanitization to prevent prompt inject
 ion\, prompt caching for cost control\, dynamic selection\, and systematic
  evaluation.\n\nSpeaker(s): Dr. Siba Mishra\, \n\nVirtual: https://events.
 vtools.ieee.org/m/580647
LOCATION:Virtual: https://events.vtools.ieee.org/m/580647
ORGANIZER:amiya87@gmail.com
SEQUENCE:11
SUMMARY:Unlocking Prompt Engineering from Tokens to RAG
URL;VALUE=URI:https://events.vtools.ieee.org/m/580647
X-ALT-DESC:Description: <br /><p>&nbsp\;Prompt engineering sits at the inte
 rsection of human intent and machine execution\, transforming raw large la
 nguage models into targeted\, reliable tools. Grounded in core model mecha
 nics: such as token processing\, probability distributions\, temperature\,
  and top-p sampling\, effective prompt design helps direct model variabili
 ty\, align output with human values\, and mitigate hallucinations. The dis
 cipline encompasses structural prompt construction using clear context\, d
 elimiters\, and desired formatting\, alongside strategies like role-playin
 g\, few-shot examples\, and step-by-step Chain of Thought reasoning. For c
 omplex domain requirements\, prompt strategies extend to Retrieval-Augment
 ed Generation (RAG) and fine-tuning. Ultimately\, building production-read
 y prompts relies on software engineering rigor\, including input sanitizat
 ion to prevent prompt injection\, prompt caching for cost control\, dynami
 c selection\, and systematic evaluation.</p>
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