Cursor
Code
Prototype and revise code with an AI editor, then review the resulting changes.
All capabilities · 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.
Explore 5 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
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
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
5 tools to explore
GitHub Copilot
Code
Explore coding assistance while checking suggestions against tests and your requirements.
Streamlit
Build · Data
Turn a Python workflow into an interactive demo that others can try.
Gradio
Build
Wrap a model function in a small interactive interface for feedback and testing.
n8n
Automate · Build
Connect triggers, API calls and model steps into an inspectable workflow.
Practice
Deployed prototype: a payment-dispute triage assistant
Build a Streamlit or Gradio app where someone pastes a payment complaint in their own words and gets back a structured classification — fraud, failed-but-debited, merchant issue — plus a drafted complaint message, powered by an LLM API. Write your own test set of 30 complaints (or generate them with a chat model), so the project never needs access to anyone's real transaction data. Scaffold most of the code with an AI coding assistant, ship it to a live URL, and put it in front of three people outside your circle.
Start from
30 payment-complaint descriptions you write or generate with a chat model, spread across fraud, failed-but-debited and merchant-dispute cases
Milestones
- Write the 30-complaint test set and the JSON schema the app must return · ~1h
- Scaffold the Streamlit/Gradio app with an AI coding assistant and get it classifying locally · ~1h
- Deploy to a public URL and run all 30 cases against the live app · ~1h
- Collect feedback from three outside testers and ship one change from it · ~1.5h
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
- A live URL a stranger can open and use with you nowhere near it
- The 30-case test set with the deployed app's classification for each, so the error rate is visible rather than claimed
- Feedback notes from three testers outside your circle, and the commit that changed the app because of them
- A README stating which parts the AI assistant wrote and how many hours the whole build took
Signal it
Shipped a live LLM-powered dispute-triage app in a weekend with an AI coding assistant — tested on 30 cases, put in front of three outside users, and iterated on their feedback.
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?