A notebook that works once is a proof of concept. Shipping five production AI apps in six months taught me the same checklist applies whether the app is a RAG platform or a fine-tuning dashboard.
The non-negotiables
- Containerise it. Docker means the same image runs in dev and in production, no "works on my machine."
- Structured logs from day one. JSON logs you can query beat print statements you have to grep.
- Metrics before you need them. You want to know something's wrong from a dashboard, not from a user's complaint.
- A test suite that runs in CI. 107 passing tests on FineTune Studio, 260+ on FaceVision — not because testing is fashionable, but because it's the only thing that lets you change code without dread.
- A fallback for every external call. Every LLM, embedding and reranking provider in RAG.NextUpgrad has a second option that kicks in automatically on failure.
The part nobody puts in the tutorial
Health checks. /healthz says the process is up. /readyz says the thing the process depends on — a vector store, a database — is actually reachable. The two are different questions, and conflating them means your monitoring lies to you exactly when you need it most.
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.
