BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//scipy-2026//talk//NWPPHA
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-NWPPHA@pretalx.com
DTSTART;TZID=CST:20260714T133000
DTEND;TZID=CST:20260714T173000
DESCRIPTION:The quality of the retrieval component is what drives Retrieval
 -Augmented Generation (RAG) systems. Therefore\, a well-structured\, measu
 rable\,  and robust retrieval pipeline is critical to building effective l
 arge language model (LLM) applications.\n\nWorking through guided code exa
 mples and hands-on experimentation\, attendees will collectively develop\,
  optimize\,  and enhance the performance of a complete RAG pipeline by imp
 roving retrieval in three stages: _Pre-Retrieval_\, _Mid-Retrieval_\, and 
 _Post-Retrieval_. We will also cover structured and multimodal document pa
 rsing with _Docling_\, systematic evaluation with _RAGAS_\, and a capstone
  _Agentic RAG_ demo using _LangGraph_. The toolkit integrates _Qdrant_ for
  vector search and the _LangChain_ ecosystem for orchestration and experim
 entation.\n\nDuring the hands-on session\, attendees will use Jupyter note
 books to learn about\, experiment with\,  and benchmark techniques that pr
 oduce significant improvements to retrieval quality using production-ready
  open-source libraries. At the end of the session\, each participant will 
 be equipped with a reusable _“Retrieval Playground”_ framework that ca
 n be leveraged to design\, evaluate\,   and continuously improve RAG syste
 ms across various application domains.\n\nInstallation Instructions: https
 ://github.com/mahimaarora/retrieval-playground/tree/main/setup-guides
DTSTAMP:20260715T023004Z
LOCATION:AI/ML
SUMMARY:Engineering Better Retrieval for RAG (Room HSEC 3-110) - Mahima Aro
 ra\, Aarti Jha
URL:https://pretalx.com/scipy-2026/talk/NWPPHA/
END:VEVENT
END:VCALENDAR