BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//scipy-2026//talk//39NQ3Y
BEGIN:VTIMEZONE
TZID:CST
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20061029T080000Z
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:STANDARD
DTSTART:20071104T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000402T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060402T090000Z
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20070311T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-scipy-2026-39NQ3Y@pretalx.com
DTSTART;TZID=CST:20260717T143500
DTEND;TZID=CST:20260717T150500
DESCRIPTION:LLMs are powerful\, but their knowledge is frozen — they can'
 t access your private documents or recent information. Retrieval-Augmented
  Generation (RAG) solves this by searching relevant documents and includin
 g them in the prompt\, grounding responses in real information. But buildi
 ng a good retrieval system involves many steps: reading diverse file forma
 ts\, chunking text at sensible boundaries\, computing embeddings\, and com
 bining search strategies. This talk introduces raghilda\, a Python framewo
 rk that handles the full retrieval pipeline. We'll cover how RAG works\, h
 ow to build a retrieval system with raghilda\, and how to connect it to an
  LLM with a practical example.
DTSTAMP:20260715T022745Z
LOCATION:Memorial Hall
SUMMARY:Retrieval Augmented Generation with Raghilda - Carson Sievert\, Dan
 iel Falbel\, Tomasz Kalinowski
URL:https://pretalx.com/scipy-2026/talk/39NQ3Y/
END:VEVENT
END:VCALENDAR