Creativity · Projects
Conversational AI projects for B.Tech students — real examples
Conversational AI builds systems that hold multi-turn dialogue with users — managing state, grounding answers in data, calling tools and handling escalation. Modern implementations are language models wrapped in retrieval, memory and control logic. The strongest B.Tech portfolios are built on real projects, not tutorials. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, Conversational AI project work runs through the AICTE IDEA Lab under faculty mentorship — here are the real patterns students build on.
At a glance
- Topic
- Conversational 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)
What students actually build
- RAG systems over VIPS-TC corpora — the flagship VSET capstone — are grounded conversational systems in practice.
- Student-built MCP servers and LangGraph orchestrators add the tool-calling and control layers such systems need.
Labs and infrastructure
- Local model inference for dialogue systems runs on the AICTE IDEA Lab GPU workstations.
- The Quantum Research Lab supports research-grade experimentation.
How VSET teaches Conversational AI
Conversational AI builds systems that hold multi-turn dialogue with users — managing state, grounding answers in data, calling tools and handling escalation. Modern implementations are language models wrapped in retrieval, memory and control logic. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).
- Every layer is documented in VSET's published curriculum at learn.engineering.vips.edu: LLMs, prompt engineering, RAG, vector databases and agent frameworks.
- The 160+ page MCP library covers how a conversational system reaches tools and data sources.
- LangChain, LangGraph, CrewAI and AutoGen are all covered, which is the orchestration layer behind multi-turn control flow.
- Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.
Frequently asked questions
What makes a good Conversational AI 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 Conversational AI project beats five tutorial clones.
Is conversational AI covered at VSET?
Yes. The published curriculum covers LLMs, prompt engineering, RAG, vector databases and agent frameworks — the full stack a dialogue system is built from.
Do students build working chat systems?
Yes. RAG systems over VIPS-TC corpora are the flagship capstone, and they are grounded conversational applications.
How is tool use handled?
Through the Model Context Protocol, which VSET documents in a 160+ page library and which students implement as MCP servers.
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