AI Toolbox - The Right Tool for Every Job
The AI tool landscape moves fast and the choices are overwhelming. This module maps the tools that actually matter for product, growth, design, and engineering roles - with a deep dive on Cursor as the highest-leverage AI coding tool for non-engineers who want to build real PoCs.
- The AI tool landscape mapped by job-to-be-done across 5 categories
- How Cursor enables non-engineers to build real prototypes in plain English
- How to choose between Claude, ChatGPT, Gemini, v0, Lovable, and Cursor for any task
- Build your first Cursor PoC from a plain-English brief
- Audit your current AI stack and identify the highest-leverage gap
- Route any task to the right tool in under 30 seconds
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AI Toolbox - The Right Tool for Every Job lesson
1. The AI tool landscape (mapped by job to be done)
Thinking & Writing
| Tool | Best for | Use it for |
|---|---|---|
| Claude (Anthropic) | PM, Growth, Designer | Research synthesis, long briefs, structured analysis, nuanced copy, complex instructions |
| ChatGPT (OpenAI) | All roles | Brainstorming, quick tasks, image analysis, breadth of capability |
| Gemini (Google) | All roles | Google Workspace tasks, real-time web search, data in Sheets |
| Perplexity | All roles | Research with live citations - replaces many search queries |
| Notion AI | PM, Growth, Designer | Synthesising notes, drafting docs, querying your existing knowledge base |
Building & Coding
| Tool | Best for | Use it for |
|---|---|---|
| Cursor | Engineer, PM (PoCs) | AI-native code editor - pair programming, PoC building, codebase understanding |
| v0 by Vercel | Designer, PM | Generate React UI components from text descriptions - no coding required |
| Lovable | PM, Designer | Full-stack app generation from prompts - what this course was built with |
| Replit AI | Engineer, PM | Browser-based coding + AI - fast PoC deployment without local setup |
| GitHub Copilot | Engineer | In-editor code completion - the industry standard for engineers |
Automation & Agents
| Tool | Best for | Use it for |
|---|---|---|
| n8n | PM, Growth, Engineer | Visual workflow automation - triggers, AI steps, tool connections |
| Make (Integromat) | PM, Growth | Similar to n8n - strong on SaaS integrations |
| Zapier | All roles | Simplest automation tool - lower ceiling but fast to set up |
| LangChain | Engineer | Framework for building multi-step AI agents in Python/JS |
| CrewAI | Engineer | Multi-agent orchestration - agents that collaborate on complex tasks |
Design & Visual
| Tool | Best for | Use it for |
|---|---|---|
| Figma AI | Designer, PM | Design suggestions, copy generation, auto-layout within Figma |
| Canva AI | All roles | Quick visual creation, presentation design, social assets |
| Midjourney / DALL·E | Designer, PM | Image generation for mockups, concept visuals, presentations |
| Framer AI | Designer, PM | AI-generated websites and landing pages - design + publish in one tool |
Research & Analysis
| Tool | Best for | Use it for |
|---|---|---|
| Perplexity | All roles | Research with cited sources - great for market research, competitor analysis |
| Elicit | PM, Growth | AI-powered literature and research synthesis |
| Rows AI | Growth, PM | AI-powered spreadsheets - query your data in plain English |
| PostHog AI | Growth, Engineer | AI-assisted product analytics |
Meetings & Async Work
This is the highest-frequency daily use case that most AI tool guides ignore. You spend 4–6 hours a day in meetings and async communication. AI can compress the overhead dramatically.
| Tool | Best for | Use it for |
|---|---|---|
| Granola | All roles | AI meeting notes - joins calls, transcribes, generates structured summaries automatically |
| Notion AI | PM, Growth, Designer | Summarise meeting notes, extract action items, draft follow-up comms from meeting context |
| Claude (with transcript) | All roles | Paste any transcript → extract decisions, actions, open questions, disagreements |
| Otter.ai | All roles | Real-time transcription + AI summary for any meeting |
| Fireflies.ai | All roles | Meeting intelligence - transcription, action items, CRM sync |
The async work unlock. The highest-leverage application isn't meeting notes - it's async context compression. Long Slack threads, email chains, Notion docs that have grown unwieldy: paste them into Claude and ask "summarise this, extract open decisions, and list what's blocking progress." That's a 40-minute reading task turned into a 2-minute brief.
For PMs specifically: end-of-sprint summaries, stakeholder update drafts, and weekly team digests are all tasks where AI removes the blank-page friction entirely. You paste the inputs (meeting notes, metrics, Slack threads), AI drafts the output, you edit and send.
Granola - the recommended default for meetings. Granola runs in the background during any meeting (Zoom, Google Meet, Teams) and generates structured notes automatically. Unlike tools that record and transcribe only, Granola uses the transcript to generate a clean, formatted summary with action items. For growth PMs who are in 4–8 meetings a day: Granola means you stop taking notes entirely and start being fully present. The notes are there when you need them. The ROI is immediate.
Agentic coding & terminal agents
| Tool | Best for | Use it for |
|---|---|---|
| Terminal coding agents (Claude Code, Codex CLI and equivalents) | PM, Engineer | Multi-file changes, repo-wide questions, long refactors that run while you review |
| IDE agents (Cursor, Windsurf, Copilot agent mode) | PM, Engineer | Building PoCs with a visual editor and inline diffs |
| Async coding agents (agents that open pull requests) | Engineer, PM | Small, well-specified tickets delegated end to end |
The pattern to learn is delegation: write a crisp spec, let the agent work, then review a diff. The specific product you use will change; the review habit won't.
Deep research & agentic browsing
| Tool | Best for | Use it for |
|---|---|---|
| Deep research modes (in ChatGPT, Claude, Gemini, Perplexity) | All roles | Multi-source market and competitor reports with citations, in 5–20 minutes |
| AI browsers & computer-use agents | PM, Growth | Filling forms, pulling data from tools without an API, repetitive click-work - always supervised |
Treat every deep-research output as a first draft with footnotes: open at least three citations before you quote a number in a strategy doc.
Spreadsheets, BI & voice
| Tool | Best for | Use it for |
|---|---|---|
| AI spreadsheets (AI formulas and enrichment inside Sheets/Excel-likes) | Growth, PM | Enriching lead lists, classifying rows, bulk summarising qualitative columns |
| BI copilots (natural-language layers on your warehouse) | PM, Growth | Self-serve metric questions without waiting on an analyst - verify the generated SQL |
| Realtime voice modes | All roles | Thinking out loud, interview practice, hands-free debriefs after a call |
3. Agentic coding in depth - Cursor as the worked example
What is Cursor? Cursor is a code editor (built on VS Code) with AI deeply integrated throughout. Unlike GitHub Copilot which completes code as you type, Cursor lets you have full conversations with your codebase - you can ask it to build features, explain code, fix bugs, and generate entire applications from a description.
For a non-engineer, Cursor is the tool that makes "I'll build a quick prototype" actually possible - without needing to understand every line of code you're working with.
Why Cursor vs v0 or Lovable?
| Cursor | v0 | Lovable | |
|---|---|---|---|
| Best for | Full apps, complex PoCs, working with existing code | React components, UI snippets | Full-stack apps from scratch |
| Requires | Basic comfort with files and a terminal | None | None |
| Output | Runs locally, fully customisable | Component code to paste | Hosted app |
| AI depth | Deepest - full codebase understanding | UI-focused | App-focused |
| Learning curve | Medium | Low | Low |
Use Cursor when: you want to build something more complex than a UI component but don't want to fully hand over control to an app generator. Great for: data dashboards, multi-step flows, PoCs that connect to a real API.
Getting started with Cursor (non-engineer path)
- Step 1 - Install Cursor. Download from cursor.com. It looks like VS Code. If you've never opened a code editor, that's fine - you'll be using the AI chat panel more than the code itself.
- Step 2 - Start a new project with AI. Open Cursor, press Cmd+Shift+P → "New File". Then open the AI chat panel (Cmd+L). Type: "Create a simple HTML and JavaScript app that [describe what you want to build]. Make it self-contained in one file. Use Tailwind CSS from a CDN." Cursor will generate the full file.
- Step 3 - Iterate in plain English. Don't edit the code manually. Instead, select the part that's wrong, press Cmd+K, and tell Cursor what to change: "Make this button green", "Add a second step to this flow", "The table isn't showing - fix it." Cursor edits the code based on your instruction.
- Step 4 - Preview your PoC. Open the generated HTML file in a browser. That's your prototype. For more complex apps, Cursor can run a local server - ask it: "How do I preview this?" and follow the instructions.
- Step 5 - Explain code you didn't write. If you receive a codebase from an engineer, open it in Cursor and ask: "Explain what this file does", "Where does the sign-up flow start?", "What would break if I changed this value?" - Cursor answers in plain English.
Practical PoC examples for PMs with Cursor
- "Build a simple A/B test calculator - inputs: control conversion rate, variant conversion rate, sample size. Output: statistical significance and recommendation."
- "Build a one-page onboarding flow mockup with 3 steps, a progress bar, and a completion screen."
- "Build a dashboard that reads from this CSV and shows a bar chart of sign-ups by week."
- "Build a prototype of a referral page with an invite input, a copy-link button, and a counter showing how many friends have been invited."
What Cursor can't do for non-engineers
- Deploy to a real URL without some engineering help (use Lovable or v0 for that)
- Connect to your production database (use it for PoCs, not production code)
- Replace a real engineer for anything that ships to users
The PM Cursor workflow in one sentence: describe what you want to build in plain English → iterate in plain English → open in browser → show stakeholders.
4. Choosing your personal AI stack
Your role-specific stack recommendation appears below ↓
Prompts for AI Toolbox - The Right Tool for Every Job
Build a Cursor PoC from a brief
I want to build a quick prototype in Cursor. Here is what I need: Goal: [DESCRIBE WHAT THE PROTOTYPE SHOULD DO] Users: [WHO WILL SEE THIS DEMO] Key interactions: [LIST 2–3 THINGS THE USER SHOULD BE ABLE TO DO] Visual style: [DESCRIBE - e.g. clean, dark theme, minimal] Build this as a single self-contained HTML file using vanilla JavaScript and Tailwind CSS from CDN. Make it look professional and polished. Prioritise the core interaction over completeness. Add placeholder data where needed. Include clear comments so I can understand what each section does.
Which tool should I use for this task?
I need to: [DESCRIBE THE TASK IN DETAIL] My role is: [ROLE] My technical comfort level is: [non-technical / some experience / comfortable with code] I need the output to be: [a shareable prototype / a local demo / production-ready / a one-off analysis] Timeline: [how long I have] Recommend the best AI tool for this specific task. Include: which tool, which tier/model, how to approach it, and any pitfalls to avoid. Be specific.
Audit my AI stack
Here is my current AI stack: [LIST YOUR TOOLS] My role is [ROLE] at [COMPANY]. My most common tasks are: [LIST 5–8 TASKS]. Audit my stack: 1. Which tools am I under-using? 2. What important use cases have no good tool coverage? 3. Where am I using the wrong tool for a task? 4. What one addition would have the highest leverage? 5. What, if anything, should I drop? Be specific and direct. Base your answer on my actual tasks, not generic advice.
Meeting transcript processor
Here is the transcript from a [TYPE - team meeting / stakeholder review / user interview / sprint planning]: [PASTE TRANSCRIPT] Extract and structure the following: 1. Key decisions made (with who made them) 2. Action items (owner + deadline if mentioned) 3. Open questions that still need answers 4. Key disagreements or tensions surfaced 5. One-paragraph summary for someone who wasn't there Return as structured markdown. Be specific - use names and direct quotes where relevant.
Async thread summariser
Here is a long [Slack thread / email chain / Notion comment thread]: [PASTE CONTENT] Give me: 1. What this thread is about (1 sentence) 2. The current state / what's been decided 3. What's still unresolved or being debated 4. Who I need to follow up with and why 5. Suggested next action (what should happen to move this forward) Be direct. If nothing is unresolved, say so.
Exercise for AI Toolbox - The Right Tool for Every Job
🛠 Exercise 1 - Build your first Cursor PoC. Install Cursor (cursor.com). Pick a growth idea or feature you've been wanting to prototype. Use the "Build a Cursor PoC from a brief" prompt to generate a working HTML file. Open it in a browser. Share it with one colleague. Time yourself - the goal is under 45 minutes from idea to demo.
🎯 Exercise 2 - Audit your current AI stack. Use the "Audit my AI stack" prompt with your honest current tool list. Identify the one gap that would have the highest leverage. Spend 20 minutes exploring the tool that fills it.
💡 Exercise 3 - Map a task to the right tool. Pick 5 tasks you've done manually in the last week that felt slow or repetitive. For each one, use the "Which tool should I use?" prompt. You'll likely find 2–3 tasks that have a much better tool than what you're currently using.
Resources for AI Toolbox - The Right Tool for Every Job
Questions about this module
How many AI tools do I actually need?
Usually three: one general assistant, one tool for building or prototyping, and one automation layer. More tools mostly add switching cost.
How do I evaluate a new AI tool?
Run one real task you already know the answer to, and compare quality, effort and trust against your current approach. Demos hide the failure cases.
Related modules
Terms used here
All prompts from this module are also on the prompt library.