Curiosity · After 12th
How to learn Large Language Models after 12th in Delhi
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. Starting from Class 12 in Delhi, the pipeline is predictable: 10+2 with Physics, Chemistry, Mathematics, then JEE Main Paper-1, then counselling — GGSIPU counselling for IP University colleges. The real decision is choosing a college whose Large Language Models coverage is genuine rather than a brochure keyword.
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)
The degree route
The degree route is a B.Tech with genuine Large Language Models depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means documented coursework depth inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.
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.
Where Large Language Models skills lead
Graduates applying Large Language Models skills typically target roles such as LLM Engineer, AI Engineer, NLP Engineer, Applied Scientist, AI Application Developer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
How admission works
Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.
Frequently asked questions
Can I learn Large Language Models after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Large Language Models-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
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