A final-year project is the one time college gives you months to build something and a panel that has to listen. Most students spend it on a chatbot with no users and a report with no numbers. Here is how I would choose, having reviewed many and having built the projects that got me hired.
Choose by user, not by technology
Start with a person who has a problem you can reach: a department, a local shop, an NGO, a clinic. The college-documents RAG project is a strong default because the users are in the building. A project with ten real users survives any viva question about relevance.
Pick one hard part
Retrieval quality, deterministic extraction, on-device inference, evaluation in a regional language — one genuinely difficult problem you will actually solve. The rest of the system should be plain engineering. Examiners and recruiters both look for the hard part; see the ideas in multimodal LLMs: five student project ideas.
Plan the evaluation in month one
- A test set with known answers, built before the system works.
- Metrics that fit: recall and faithfulness for retrieval, field accuracy for extraction, error rates per group for recognition.
- A baseline to compare against — the naive version, or the tool users have today.
Scope for a demo
One demo path that runs live on the examiner's input. Everything else in the report as future work. A narrow, working system with numbers beats a broad architecture diagram every time, at a viva and at an interview.
Document as you go
- A README that a recruiter can read in three minutes — see a GitHub profile recruiters actually read.
- Weekly notes on decisions and failures; they become the report's best chapter.
- A short demo video for people who cannot run it.
Done this way, the final-year project is the first item on your resume and the story you tell in every interview. Done the usual way, it is a PDF nobody opens again. The difference is decided in the first month.
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.
