Curiosity · Syllabus
Document AI in a B.Tech — syllabus & what you learn
Document AI extracts structure and meaning from unstructured documents — parsing, layout understanding, entity extraction, classification and question answering over PDFs and scans. It combines OCR-style vision with language modelling. Inside a four-year B.Tech, Document AI arrives in layers: programming and mathematics foundations in years one and two, core methods next, and applied depth concentrated in years three and four plus the capstone. Using Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura's B.Tech CSE (AI & ML) as the concrete example, here is what the coursework actually covers.
At a glance
- Topic
- Document AI
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
How VSET teaches Document AI
Document AI extracts structure and meaning from unstructured documents — parsing, layout understanding, entity extraction, classification and question answering over PDFs and scans. It combines OCR-style vision with language modelling. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- The core techniques are documented at learn.engineering.vips.edu: NLP, computer vision, RAG, embeddings and vector databases.
- RAG is taught explicitly as the technique for answering questions over a document corpus, which is the dominant document-AI pattern.
- The MCP library covers how document stores are exposed to a model as callable tools.
- Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.
Labs and infrastructure
- Embedding generation over document corpora and local model inference run on the AICTE IDEA Lab GPU workstations.
What students actually build
- The flagship VSET capstone — RAG systems built over VIPS-TC corpora — is a document-understanding system end to end.
- Applied NLP tools are a documented capstone category covering extraction and classification work.
Frequently asked questions
When does Document AI content actually start in a B.Tech?
Meaningful Document AI content typically ramps up from the second or third year, after programming and mathematics foundations. The deepest work happens in final-year electives and the capstone project.
Does VSET cover document understanding?
Yes — through NLP, computer vision, RAG, embeddings and vector database material published at learn.engineering.vips.edu.
What is the hands-on component?
The RAG capstone over VIPS-TC corpora, which requires ingesting, chunking, embedding and querying a real document collection.
Is OCR taught specifically?
Computer vision is a documented topic that covers the image-understanding side; document pipelines are assembled from that plus the taught NLP and retrieval material.
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