3 min read
From idea to deployed: shipping a full-stack AI project
A practical checklist for taking an AI prototype to something other people can use.
Most AI projects die as a notebook. Here is the checklist I use to get one in front of real users.
Make it reproducible
- Pin dependencies and document the environment variables.
- One command to run everything locally:
docker compose up.
Protect the edges
- Validate every input on the server, not just in the form.
- Rate-limit the endpoints that call a paid model.
- Keep secrets out of the repo and out of the frontend bundle.
Make failure visible
- Structured logs with a request id.
- A health endpoint and a friendly error page.
- Show users a clear message when the model is slow or unavailable.
Ship small, ship often
Deploy the thinnest working version first. A live link teaches you more than another week of polishing locally.
Write it down
A short README with a screenshot, the architecture in one diagram and a "how to run" section is often what a recruiter reads first.
Blog
4 min read
Building a RAG helpdesk that cites its sources
How retrieval, reranking and a strict prompt turn an LLM into an IT helpdesk agent people can actually trust.
- RAG
- LLM
- FastAPI
3 min read
Making a portfolio feel 3D without killing performance
Three.js, CSS perspective and a few rules that keep a 3D site fast on a normal laptop.
- Three.js
- Next.js
- Performance