Build an AI Portfolio¶
In one line: Choose three or four projects that show different skills, present each with a clear README, numbers and an honest limitations section, and link to hands-on projects on this site to start. Last reviewed: 2026-11-07
The short version¶
- A portfolio is evidence that you can finish things. Three or four good projects beat ten half-built ones.
- Cover different skills: one that retrieves, one that evaluates, one that ships, and one that shows judgment about risk.
- Every project needs a README that says what it does, how it was measured, what failed and what it costs to run.
- This is judgment from this site's editors. No hiring-survey data is cited.
What to build¶
| Project type | Starting point here | What it proves |
|---|---|---|
| RAG over real documents | Build a RAG pipeline | Retrieval, chunking, citations |
| Structured extraction | Structured extraction pipeline | Schemas, validation, error handling |
| Agent with tools | Claude agent with MCP, coding agent loop | Tool use, loops, limits |
| Evaluation | Set up an eval harness, evaluate an agent end to end | Measuring quality |
| Classical ML | Tabular ML end to end, deploy an ML model | Baselines, deployment |
| Fine-tuning and serving | Fine-tune with LoRA, Llama on one GPU | Training and inference |
| Security | Prompt injection red team | Risk awareness |
| Voice or chat product | Voice agent, chatbot in an afternoon | Product thinking |
Do not publish an unchanged tutorial result as your own. Change the data, the question or the evaluation so the work is clearly yours.
How to present each project¶
- One-line summary and a screenshot or short demo.
- Problem and users. Who needs this and why.
- Design. One diagram, the main choices and the alternatives you rejected.
- Results. Numbers from an eval set you built, compared with a simple baseline. Say how large the set was.
- Failures and limits. What it gets wrong, with examples. This is where reviewers learn to trust you.
- Cost and latency. What one task costs and how long it takes.
- How to run it. One command, a
.env.example, and no committed secrets.
Make it safe to publish¶
- Use data you are allowed to share. Never publish an employer's or client's material.
- Keep keys out of git. See API keys and env vars.
- Note any security work you did: prompt injection defense, OWASP LLM Top 10.
Where to put it¶
A public GitHub repository per project, a short personal page that links to them, and a pinned profile README. Keep it current; remove broken demos.
Match the portfolio to the role¶
- AI engineer: RAG, agent, eval, plus one deployed app.
- ML engineer: tabular model, deployed model, fine-tune.
- Data scientist: extraction plus a statistical analysis with honest uncertainty.
- AI product manager: a spec, an eval you built and a working prototype.
- Researcher: a reproduction with a write-up.
Common mistakes¶
- Only chatbot wrappers with no evaluation.
- No baseline, so nobody can tell if the AI helped.
- Hiding failures. Reviewers find them anyway.
- Demos that die because an API key expired.
Next steps¶
- Practise explaining a project with the AI engineer interview and AI system design interview.
- AI certifications guide for when a credential adds to a portfolio.
Sources¶
- Judgment: what to include is editorial, based on the projects on this site.
- Project pages linked above hold the runnable detail.