Product, business & communication
Prototype an AI feature without an engineering team
Build a clickable/working prototype with no-code tools, prompt playgrounds or AI coding assistants.
~10 focused hours·beginner
Tools: Claude/ChatGPT + code artifacts, Cursor/GitHub Copilot, Streamlit/Gradio, n8n or Bubble for no-code flows
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Build a clickable or working prototype without waiting for an engineering sprint
- Use an AI coding assistant to scaffold a working demo from a product spec
- Wire an LLM API into a simple Streamlit/Gradio UI to test a concept with real users
- Prototype in a prompt playground before committing to a build
- Use no-code tools to fake the backend so the front-end story can be user-tested
- Iterate a prototype same-day based on stakeholder or user feedback
Needs first: Explain how LLMs work and where they fail
Learn — free, link-checked
The few resources that matter
Read · beginner · 30 min · docs.anthropic.com
Prompt Engineering Overview
Official, up-to-date techniques (system prompts, few-shot, chain-of-thought) with before/after examples you can copy. — Anthropic
Course · beginner · 90 min · deeplearning.ai
ChatGPT Prompt Engineering for Developers
Hands-on notebook exercises that turn prompting principles into a repeatable, testable workflow. — DeepLearning.AI (Andrew Ng, Isa Fulford)
Course · beginner · 180 min · microsoft.github.io
Generative AI for Beginners
21 free lessons with code you run yourself — the fastest path from zero to a working prototype. — Microsoft
Build from · intermediate · 90 min · github.com
OpenAI Cookbook
Battle-tested example notebooks (chat apps, vision, retries) you can lift directly into a real project. — OpenAI
Build from · intermediate · 120 min · github.com
Anthropic courses
Anthropic's own hands-on notebooks for tool use, structured extraction, and grading model output quality. — Anthropic
Practice
Working prototype: UPI transaction dispute assistant
Build a Streamlit or Gradio app where a user pastes a UPI transaction description and gets back a structured dispute-classification (fraud, failed-but-debited, merchant issue) plus a drafted complaint message, powered by an LLM API. Use an AI coding assistant to scaffold most of the code. Ship it to a live URL and get at least 3 people outside your immediate circle to try it and give feedback.
Done when
- App is deployed to a public URL (Streamlit Community Cloud, HF Spaces, or similar) and works end to end
- At least 3 external testers used it and gave feedback, documented in the README
- README states which parts were AI-assisted and roughly how long the build took
- One iteration based on tester feedback is visible in git history
Prove it
Evidence a recruiter can check
- Live deployed prototype URL
- Public GitHub repo with README describing the build process and AI-assistant usage
- Tester feedback notes and the resulting change log
Interview
Questions you'll get asked
- Tell me about a prototype you built without an engineering team — what tools did you use?
- How would you validate a new AI feature idea before asking engineering for a sprint?
- Walk me through how you'd use an AI coding assistant to build a working demo in an afternoon.
- What's the difference between a prototype meant for user testing and one meant for a stakeholder demo?
- How do you decide when a prototype is 'good enough' to hand off to engineering?