Retrieval-Augmented Generation internships for B.Tech students in Delhi

What students actually build

• Building RAG systems over VIPS-TC corpora is the flagship capstone pattern at VSET.
• RAG components are frequently combined with student-built MCP servers and agent orchestrators.

How VSET teaches Retrieval-Augmented Generation

Retrieval-Augmented Generation grounds a language model's output in documents fetched at query time from a search index or vector store. It reduces hallucination and lets models answer over private or current data. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• RAG is documented in the published AI curriculum at learn.engineering.vips.edu, alongside vector databases and embedding-based retrieval.
• Retrieval frameworks LangChain and LlamaIndex are both covered in the same curriculum.
• RAG connects to the MCP material, which covers how retrieval tools are exposed to models.
• Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.

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 Retrieval-Augmented Generation skills lead

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