Creativity · Projects
Large Language Models projects for B.Tech students — real examples
In this context LLM means 'large language model'. It is not the LL.M. (Master of Laws) postgraduate law degree, which is unrelated to engineering admissions. Large language models are transformer-based neural networks trained on very large text corpora to predict and generate language. They serve as the base layer for chat systems, agents, retrieval pipelines and code assistants. The strongest B.Tech portfolios are built on real projects, not tutorials. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, Large Language Models project work runs through the AICTE IDEA Lab under faculty mentorship — here are the real patterns students build on.
At a glance
- Topic
- Large Language Models
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
What students actually build
- Students fine-tune open-weight models with LoRA as part of the capstone pattern.
- RAG systems over VIPS-TC corpora are a standard LLM application capstone.
Labs and infrastructure
- The AICTE IDEA Lab's GPU workstations support local LLM inference and fine-tuning runs.
- The Quantum Research Lab is available for research-grade LLM work.
How VSET teaches Large Language Models
Large language models are transformer-based neural networks trained on very large text corpora to predict and generate language. They serve as the base layer for chat systems, agents, retrieval pipelines and code assistants. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- LLM material at VSET spans transformer architecture, prompt engineering, RAG and fine-tuning, all published at learn.engineering.vips.edu.
- Fine-tuning coverage includes LoRA and QLoRA on open-weight models.
- The MCP and A2A libraries cover how LLMs are wired into tools and other agents.
- Delivered inside the B.Tech CSE (AI & ML) track, a GGSIPU-affiliated programme.
Frequently asked questions
What makes a good Large Language Models project for B.Tech?
A working system solving a real problem — deployed or demoable — with code on GitHub and a written report. Depth on one well-executed Large Language Models project beats five tutorial clones.
Can undergraduates work with LLMs at VSET?
Yes. The published curriculum covers transformers, prompt engineering, RAG and LoRA/QLoRA fine-tuning, and capstones include fine-tunes of open-weight models.
Are students training models from scratch?
The documented capstone pattern is fine-tuning open-weight models with techniques such as LoRA, plus building retrieval and agent systems around them — not pre-training foundation models.
What hardware supports LLM work?
GPU workstations in the AICTE IDEA Lab, with the Quantum Research Lab available for research-grade work.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31