Context-Aware Large Language Models in Practice: Prompting and Retrieval-Augmentation Generation

From: 200.00

Last date of Registration : 24th June, 2026

SKU: IIT Mandi | Dates : 30th June & 1st July, 2026 Categories: , ,

Description about the workshop

This workshop is designed to help the participants understand how to build and leverage LLMs that can use additional information, such as conversation history or documents and databases to generate more accurate and relevant responses. This workshop shall be useful for learners across disciplines. Students and researchers shall learn to access more relevant explanations according to their learning requirements. Faculties can obtain better teaching material, and more up-to-date content. Industry professionals shall gain perspective on efficient use of domain-specific knowledge and better decision making.

Profile of the Instructor

Dr. Indu Joshi is currently working as Assistant Professor with School of Computing and Electrical Engineering at IIT Mandi, Himachal Pradesh. She has received PhD from IIT Delhi and Master of Technology from NIT Delhi. She was a postdoctoral researcher at Inria Sophia Antipolis, France and Technical University of Munich, Germany. She is an INAE student member and a Raman-Charpak Fellow. She has represented India at BRICS Young Scientist Conclave and has also attended the Heidelberg Laureate Forum. She has also been awarded for her popular science writing skills by the science and technology minister of India. She is a DAAD-Post Doc Net-AI fellow and a recipient of the Institute silver medal by the President of India for her M.Tech Degree.

Session Details

Dates of the Workshop : 30th June & 1st July, 2026

Mode of the Workshop : Online

Timings (IST) : 06:00 pm to 08:00 pm 

Fee for the Workshop

Students - Rs. 236 ( Rs. 200 + 18% GST )

PhD scholars -  Rs. 354 ( Rs. 300 + 18% GST )

Faculty - Rs. 590 ( Rs. 500 + 18% GST )

Industry - Rs. 944 ( Rs. 800 + 18% GST )

Modules of the workshop

Day Module name Concepts covered Live sessions - No. of hours Assessment Learning
outcomes
1 Prompt
Engineering
In context learning in LLMs, Few-shot prompting, chain of thought prompting, Tree of Thought
Prompting, Hands demonstrations of the above
2 Through
Quiz
Obtaining
improved
reasoning
and task
performanc e in LLMs
2 Retrieval
Augmented
Generation
(RAG)
RAG overview,
Retrieval
Technique,Prompt Caching
2 Through
Quiz
Obtaining
improved
efficiency,
relevance,
and
performanc e in LLM
based
applications

Intended Audience & Eligibility

Intended Audience : 

Students (Bachelors, Masters & PhD), Faculty and Industry professionals

Eligibility : 

12th pass

Certification Criteria

Attendance is mandatory for the certification

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