Workflow
AI Product Requirements Workflow
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.
Build a SaaS MVP with AI
The full path from idea to a shipped SaaS MVP — define and scope the requirements, design the architecture, API, and data model, then build it reviewed, tested, secured, cost-controlled, and deployed.
Overview
Most 'build a SaaS with AI' advice is a list of prompts with no spine. This project is the spine, and it now runs the whole arc — idea → architecture → build → validate → ship. It walks the journey a real team takes: figure out what you're building, cut it to a first release small enough to ship, then design the parts that are expensive to reverse — the architecture, the API contract, and the data model — before writing them into code. From there it builds with the discipline that keeps an MVP from becoming a liability: reviewed, tested, secured, with its AI costs under control, and finally deployed with a rollback instead of left on a branch. Each stage connects to a NewPrompt workflow you can run on its own; together they carry an idea from a sentence to something running in front of users. You bring the product judgment and write the code — the project makes sure no stage, including the one most 'MVP' guides skip (shipping it), gets left out.
The journey
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Define & Scope
Clarify what you're building and for whom.
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Define what you're actually building
Before any code, turn the idea into written requirements — what the product must do, for whom, with acceptance criteria — so the whole team builds the same thing.
Outcome A requirements document the build can start from. -
Cut it to an MVP
Requirements describe the whole product; an MVP ships a slice. Separate the one core value the first release must prove from everything that can wait.
Outcome A first-release scope small enough to actually ship, with a success signal.
Design
Design the right solution before building.
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Design the architecture
Decide how you'll build the scoped MVP — boundaries, data flow, the trade-offs that are expensive to reverse — and write the decisions down before the first line of code.
Outcome An architecture decided on its trade-offs and documented. -
Design the API contract
Pin the surface the product is built on — resources, endpoints, payloads, validation, versioning — as a contract before code makes it permanent, so the frontend and backend develop against something stable.
Outcome An API contract the build develops against. -
Design the data model
Model the entities, relationships, and constraints the MVP runs on, and plan the indexes the real queries need — the schema is the hardest thing to change later, so design it on its data before launch.
Outcome A schema designed on its entities, constraints, and queries.
Build & Refine
Build, test, secure, and make it production-ready.
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Review the code as you build
As features land, review them against your conventions with severity-ranked findings — so the MVP's codebase doesn't accumulate the debt that makes v2 miserable.
Outcome Changes reviewed for correctness and design before they merge. -
Lock behavior down with tests
Build a test suite that fails for real reasons across unit, integration, and edge cases — so you can keep shipping fast without breaking what already works.
Outcome Tests that catch real regressions, not green decoration. -
Run a security pass before launch
An MVP is still on the internet. Review the auth and input paths the way an attacker would, and back the findings with tests, before real users (and real data) arrive.
Outcome The exposed paths reviewed for vulnerabilities and guarded by tests. -
Keep the AI feature costs in check
If the SaaS uses AI, the token bill scales with usage. Measure the cost per call, trim the waste, and re-measure — so a feature that's cheap in testing doesn't break the unit economics at scale.
Outcome AI feature costs measured and trimmed before they hit production volume.
Ship & Validate
Ship with confidence and validate results.
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Ship the first release
Cross the gap from 'works on my branch' to 'live for users' — assess release readiness, plan the deploy and its rollback, and set up monitoring — so the MVP actually ships, deliberately, instead of stalling at done-but-not-deployed.
Outcome The MVP deployed with a rollback path and monitoring. -
Validate it against its success signal
Shipping isn't the finish line — find out whether the MVP did what it was for. Measure real usage and feedback against the success signal you set when you cut the scope, render a hit/partial/miss verdict, and decide whether to iterate, pivot, or scale before pouring more in.
Outcome A validation verdict on the shipped MVP and an iterate / pivot / scale decision.
Expected outcome
A SaaS MVP that's deliberately scoped, deliberately architected, and actually shipped — its API and data model designed up front, the code reviewed, tested, secured, and cost-controlled, and the release deployed with a rollback — a first version running in front of users, not a pile of AI-generated code stuck on a branch.
Best for
- Founders and developers taking a SaaS idea to a first shipped release
- Anyone who wants the whole arc — scope, architecture, build, ship — not just prompts
- Building with AI in the loop while keeping product judgment human
Not for
- A non-software product or a pure content project
- Teams with a shipped product that only need one workflow
FAQ
Do I need to be a developer to follow this?
You need to read or write code at some level — this project ships a real SaaS, not a no-code demo. The AI accelerates each stage, but you bring the product judgment and own the build. Non-developers will get more from the individual planning workflows than the full journey.
How long does the whole journey take?
It spans the whole build, not an afternoon. Each stage maps to a NewPrompt workflow that runs in an hour or two; together they carry an idea from a sentence to something running in front of users. You can pause between stages — the output of each feeds the next.
Is this just a list of prompts?
No. It is an ordered journey with a spine: define and scope, design the architecture and data model, then build it reviewed, tested, secured, and deployed. Each stage links a real workflow, resource, and tool — the structure is the point.
What should a SaaS MVP include?
A SaaS MVP should include exactly one core value the first release must prove, plus a success signal to measure it against. This blueprint's first two stages do that cut: turn the idea into written requirements with acceptance criteria, then separate that one must-ship slice from everything that can wait.
What do I need before building a SaaS MVP with AI?
Before you start, you need a SaaS idea worth scoping and the ability to read or write code, since stage one turns that idea into written requirements with acceptance criteria. You also need your own AI tools to run each workflow; the blueprint supplies the prompts, resources, and ordered path, not the model.
Does this blueprint build the SaaS app for me?
No. This blueprint does not build or ship the SaaS app for you. It gives you the ordered path, prompts, and workflows across all eleven stages — from requirements and MVP scope through architecture, tests, security, deploy, and validation — but you write the code in your own AI tools and own every product decision.
Workflows in this project
Workflow
AI MVP Planning Workflow
Cut a product idea down to the smallest first release that proves the core value — separate the real must-haves from everything that can wait, then define the MVP and its success signal.
Workflow
AI Project Architecture Workflow
Design a system's architecture on its real trade-offs instead of a confident diagram — put the model in an architect's seat, work the decisions one at a time, and write down the why.
Workflow
AI API Design Workflow
Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in.
Workflow
AI Database Design Workflow
Design a schema on its data, not a hunch — model the entities and relationships, set the constraints that protect integrity, plan indexes around real queries, then document the schema and migration.
Workflow
AI Code Review Workflow
A complete AI-assisted review pass — not one prompt — that ends with ranked findings, tests guarding behavior, and a refactor plan when one is warranted.
Workflow
AI Test Generation Workflow
Build a test suite that fails for real reasons, not green decoration — coverage across unit, integration, and edge cases, then a review for the gaps.
Workflow
AI Security Review Workflow
Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.
Workflow
AI Cost Optimization Workflow
Cut what an AI feature costs without dumbing it down — price the prompt as it runs today, see where the tokens go, trim the waste, and re-measure to prove the saving holds at scale.
Workflow
AI Deployment & Release Workflow
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
Workflow
AI Product Validation Workflow
Find out whether the thing you shipped actually worked — define the success metric, plan the measurement, classify the real evidence, then render a verdict and an iterate / pivot / scale decision.
Resources used in this project
Resource
Product Requirements Assistant
Turn a rough feature idea into a structured requirement: problem statement, acceptance criteria, and what's explicitly out of scope.
Resource
Startup Advisor Role Prompt
An expert startup advisor role prompt — stage-aware, capital-efficiency-minded, and built to name the riskiest assumption in any plan instead of cheering it on.
Resource
Software Architect Role Prompt
Make AI act as a software architect — system design, scalability, service boundaries, and trade-offs — instead of jumping straight to code.
Resource
API Review Checklist Prompt
Architecture review for the public surface: abstractions that earn their place, dependency direction, seams, and the patterns the codebase already has.
Resource
Missing Index Analysis — Which Indexes, at What Cost
Map every predicate, join, and sort to the index that serves it — or doesn't. Composite order rules, covering decisions, and the write tax nobody mentions.
Resource
Code Review Prompt — the Review Contract
"Review this code" gets shallow comments. The review contract gets findings with severities, a checklist, and a verdict.
Resource
Unit Test Prompt — Isolation Done Right
Mock the dependencies, test the business logic, one behavior per test — the unit testing contract that bans plumbing tests.
Resource
Security Code Review Prompt
Twelve security checks — injection, auth, secrets, SSRF, privilege escalation — reviewed the way an attacker would read the code.
Resource
Estimate Cost per 1,000 Calls
A classification call costs a fraction of a cent — until you run a million of them. This prices a small repeated prompt at the scale that actually bills.
Resource
Production Readiness Review Prompt
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Resource
Release Readiness Checklist Prompt
A go/no-go checklist for a release — config, secrets, migrations, comms, rollback, and monitoring all confirmed ready before you ship, ending in a clear GO or NO-GO.
Resource
Rollback Plan Prompt
Turn a deploy into a deploy you can undo — a step-by-step rollback plan with triggers, the reverse of every forward step, and the point of no return named.
Resource
Product Validation Report Prompt
Consolidate the whole validation into one shareable report — the success metric, the evidence, the hit / partial / miss verdict, and the iterate / pivot / scale decision, in a form the team and stakeholders can act on.
Resource
Turn a JSON Schema into a Prompt
You have the schema — fields, types, requirements. The translation into a prompt the model actually follows: schema lines, realistic example, and validation rules.
Resource
Schema Design & ERD Prompt — Entities, Relationships, and Keys
Turn product requirements and key entities into an ERD-ready schema spec: tables, fields, primary and foreign keys, relationships with cardinality, indexes, and constraints — with the modeling gaps named as open questions.
Tools used in this project
Tool
System Prompt Generator
Build structured system prompts from role, tone, constraints, and model target.
Tool
Role Prompt Generator
Generate expert role prompts — perspective, responsibilities, and decision criteria, not just "act as".
Tool
JSON Output Prompt Builder
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Tool
SQL Optimization Prompt
Build evidence-based SQL optimization prompts — goal, platform, and the evidence you have turn into a query tuning contract.
Tool
Code Review Prompt Generator
Generate code review prompts for any scope — snippet, file, diff, or full pull request — with focused review rules.
Tool
Test Case Prompt Generator
Build test generation prompts — unit, integration, or E2E — with framework modes and edge-case coverage rules.
Tool
Token Counter
Estimate how many tokens a prompt is and what it costs — honest ranges across GPT, Claude, and Gemini, with per-call and per-1,000-call pricing.
Tool
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Guides for this project
Guide
How to Break a Large AI Task Into Safer Smaller Prompts
Ask AI to do six things in one prompt and you get a long answer that's thin everywhere — with a wrong assumption in the first paragraph quietly shaping the rest. Here's how to turn one big request into a sequence of small prompts you can review one at a time.
Guide
How to Write a Product Requirements Document (PRD) With AI
A PRD earns its keep by separating the feature somebody asked for from the problem they actually have — then fixing scope, numbering the requirements, and handing the open questions to the people who own them.
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