Most student AI side projects stop at 'call the API, show the response' — which teaches almost nothing about the actual job of shipping a production AI system.
Project ideas chosen for what they force you to learn
- A RAG app over your own college's documents — forces chunking, retrieval evaluation and a confidence gate, not just prompting.
- A rate-limited public API wrapping a model — forces auth, rate limiting and cost control from day one.
- A background-job pipeline processing uploaded documents — forces async architecture and error handling for slow tasks.
- A small fine-tuned classifier deployed behind an API — forces the full train-evaluate-deploy loop, not just a notebook experiment.
The filter to apply to any project idea
Ask what it forces you to learn beyond calling an API. If the honest answer is 'nothing', pick a different project — the model call is rarely the part that teaches you anything a recruiter will ask about.
See shipping side projects that survive demo day.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
