Find Your Ideal AI Setup

How This Guide Works

Instead of prescribing one universal setup, this page gives you prompt packs for a planning conversation with a capable assistant. Use Claude, ChatGPT, Gemini, NotebookLM when the decision depends on dense source material, or a workspace assistant you already trust when it has enough context for the decision.

Freshness note: These prompts were updated on April 23, 2026 to reflect current AI workflow choices, including source-grounded notebook workflows, shared workspace agents, builder-layer agent platforms, chat-planner-plus-execution, editor-first, terminal-first, and privacy-first setups.

Prompt 1: Beginner Project Path

Use this if you are new to software building and need the fastest low-risk route.

I want help choosing my first AI-assisted development workflow.

My situation:
- Experience level: [none / beginner / hobbyist / working developer]
- What I want to build: [landing page / internal tool / SaaS MVP / dashboard / other]
- Preferred working style: [browser-only / editor is okay / terminal is okay]
- Budget: [free only / low monthly budget / flexible]
- Need for privacy or compliance: [none / moderate / high]
- Team policy constraints: [personal project / company-managed devices / strict approval rules]
- Do I want to learn the code deeply or just ship: [ship first / learn while building / deep ownership]

Please recommend:
1. Which workflow lane I should start in: build-first, workspace-agent-first, chat-planner-plus-execution, editor-first, terminal-first, or privacy-first
2. One primary tool choice and one backup option
3. What I should avoid at my current level
4. The first 3 setup steps to take this week
5. Which Signal Lens-style topics I should study next: UI prompting, Git workflow, guardrails, local models, deployment, or coding-model selection

Prompt 2: Working Developer Upgrade Path

Use this if you already code and want AI to improve your current workflow without causing chaos.

I already develop software and want to add AI assistance without damaging my review discipline.

My situation:
- Primary editor or IDE: [VS Code / Cursor / JetBrains / Neovim / other]
- Typical repo size: [small app / medium product / large monorepo]
- Main languages and frameworks: [list]
- Team size: [solo / small team / larger org]
- My preferred execution style: [inline help / chat beside editor / terminal agent / mixed]
- Do I want an open-weight or local-capable execution option: [no / maybe / yes]
- Approval model: [I can move fast solo / PR review required / strict approvals]
- Biggest pain points: [debugging / tests / refactors / code review / docs / incident response]
- Model preference if any: [OpenAI / Anthropic / Google / open-weight / no strong preference]

Please recommend:
1. My best workflow lane and why
2. A practical stack for planning, execution, and review
3. When I should stay in chat, use a workspace-native agent, stay in the editor, or hand work to a terminal or background agent
4. The guardrails I should put in place before I scale usage
5. A 7-day trial plan with one experiment per day

Prompt 3: Connected Workspace Setup

Use this if your work mostly lives in documents, meetings, tickets, and recurring team coordination.

I want an AI workflow that works inside the tools where my team already operates.

My situation:
- Main work systems: [Notion / Google Workspace / Microsoft 365 / Slack / Jira / mixed]
- Main recurring work: [weekly briefs / meeting follow-through / onboarding / support triage / stakeholder updates / SOP maintenance]
- Does the workflow need to run on a schedule or trigger: [no / maybe later / yes]
- What must stay human-approved: [external messages / record updates / customer-facing outputs / all writes / other]
- Source of truth: [Notion / Drive / SharePoint / Jira / mixed]
- Is the job mostly synthesis or deterministic routing: [mostly synthesis / mixed / mostly deterministic]
- Do I need a source-grounded notebook layer before drafting: [no / maybe / yes]
- Write permissions available to AI: [read only / draft only / limited approved writes / broad writes]
- Audit need: [basic run history / formal audit trail / strict compliance review]
- Team maturity: [solo / small team / larger org]
- Data sensitivity: [low / moderate / high]

Please recommend:
1. Whether I should use a workspace-agent-first workflow or a classic automation workflow
2. Which tool should own the workflow
3. Where source context should come from, whether a source-grounded notebook layer like NotebookLM should sit upstream, and whether I should use a native workspace layer, ChatGPT Workspace Agents, or Microsoft Copilot Studio
4. Where writebacks should land
5. What approval and audit rules I need before this runs on a schedule

Prompt 4: Privacy-First Engineering Setup

Use this if data handling matters as much as capability.

I need an AI-assisted engineering workflow with strong privacy and governance controls.

My situation:
- Device and hardware: [OS / RAM / GPU if any]
- Data sensitivity: [public code / internal proprietary / regulated / mixed]
- Deployment preference: [fully local / mostly local / hybrid local+cloud]
- Team policy: [solo / small team / enterprise controls required]
- Must-have tasks: [coding help / code review / planning / document analysis / automation]
- Approval expectations: [manual review for all changes / review only for risky tasks / flexible]

Please recommend:
1. Whether I should use a local-only or hybrid workflow
2. Which local runtime or server path fits best
3. Which cloud tasks are still reasonable to keep remote
4. What guardrails, instruction files, and review rules I need
5. What tradeoffs I am accepting compared with frontier cloud-only tools

Prompt 5: Budget-Conscious Stack Selection

Use this if you want the most useful stack without overlapping subscriptions.

Help me design a cost-aware AI engineering stack.

Inputs:
- Monthly budget: [free / low / medium / high]
- My main work: [learning / freelance / internal tooling / product engineering]
- Must-have capabilities: [editor help / terminal agent / local model option / deployment help / research]
- Nice-to-have capabilities: [background agents / MCP integrations / desktop planning app / scheduled workspace workflows]
- Would a source-grounded notebook layer help: [no / maybe / yes]
- What I already pay for: [list]
- Team policy: [solo / team / enterprise]
- Approval expectations: [manual signoff on all writes / signoff on risky writes / light review only]
- Preferred system of record: [workspace docs / issue tracker / CRM / shared drive / mixed]

Please recommend:
1. A lean starter stack
2. Which tools overlap too much to justify paying for both
3. Where to spend for the biggest practical upgrade
4. A cheaper fallback for every paid recommendation
5. What I should revisit once my workflow matures

How To Use The Answers Well

Ask the model to explain why it chose one lane over another.
Ask for a second-best option so you can compare tradeoffs.
If you work on teams, add your review and approval rules before taking recommendations seriously.
If the work is source-heavy, ask whether NotebookLM should sit before the main workflow. If the work is workspace-heavy, ask whether the answer should be a native suite layer, ChatGPT Workspace Agents, or Microsoft Copilot Studio. If the work is terminal-heavy, ask whether Qwen Code or Mistral Vibe changes the recommended stack.
Treat the result as a draft plan, then compare it against Choosing Your First AI Workflow, Connected Workspace AI Workflows, and Guardrails for AI Coding Agents.