Chunking Strategies internships for B.Tech students in Delhi

What students actually build

• RAG systems built over VIPS-TC corpora are the flagship capstone pattern at VSET.
• Chunk size and overlap are among the first parameters students tune when RAG answers are poor.

How VSET teaches Chunking Strategies

Chunking decides how source documents are cut into retrievable passages — by fixed size, by structure, or by semantic boundary — and how much they overlap. It quietly determines whether a RAG system retrieves anything useful. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & DS).

• Chunking is part of the RAG and retrieval material published at learn.engineering.vips.edu.
• It is taught with embeddings and vector databases, since chunk size directly changes what an embedding represents.
• LangChain and LlamaIndex, both documented in the curriculum, provide the splitting components students work with.
• Delivered inside the B.Tech CSE (AI & DS) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

How students find them

Two channels, used together: the VIPS-TC placement cell, which coordinates campus internship drives, and direct outreach — applying to startups and labs with a specific project to point at. Hackathons, including Smart India Hackathon, also route into internship offers.

Where Chunking Strategies skills lead

Graduates applying Chunking Strategies skills typically target roles such as AI Engineer, Search / Retrieval Engineer, Data Engineer, LLM Application Developer, NLP Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.