Capstone production AI product
One deployed project that combines retrieval, tools, evals, security, cost tracking, and documentation.
In plain terms
This is the portfolio artifact: a real URL, a public repo, and a write-up that explains the tradeoffs instead of only showing code.
Why it matters
Hiring teams trust finished systems more than tutorials because finished systems expose messy tradeoffs.
How it works
Build a focused product with streaming chat, RAG with citations, one tool, one guardrail, tracing, a 30-case eval, and a README with architecture and cost notes.
When you use it
After the roadmap phases, spend three or four weeks finishing this instead of starting another half-project.
Common mistakes
- Scope creep.
- Skipping the write-up.
- Building for an imaginary user.
Best practices
- Write the one-paragraph spec first.
- Ship a small v1 in week one.
- Publish real eval numbers and what broke.
Try it yourself
Write the capstone spec, three success metrics, and the first 10 eval cases before building features.