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 an API Backend with AI
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
Overview
A backend is the part of a product clients build against and can't easily walk back — every endpoint shape, every table, every auth assumption becomes a contract the moment something depends on it. This project builds that backend deliberately, and only the backend: no UI, no marketing site, just the API and the system behind it. It runs the real path an experienced team takes — pin down what the API must do, decide the architecture, design the contract and the data model before writing them into code, then build with the discipline that keeps a backend trustworthy: reviewed, tested, security-checked, and shipped with a rollback. Each stage connects to a NewPrompt workflow you can run on its own; together they carry an API from a requirement to a running service clients can depend on. You write the code and own the calls; the project makes sure the load-bearing decisions happen in the right order.
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 the backend must do
Before any design, turn the need into written requirements — the operations the API exposes, the rules, the acceptance criteria — so the whole build aims at the same target.
Outcome Requirements the backend build can start from.
Design
Design the right solution before building.
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Design the architecture
Decide how the backend is structured — 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 clients depend on — resources, endpoints, payloads, validation, and a versioning rule — as a contract before code makes it permanent.
Outcome An API contract clients can build against. -
Design the data model
Model the entities, relationships, and constraints behind the API, and plan the indexes the real queries need — the schema is the hardest thing to change later, so design it on its data.
Outcome A schema designed on its entities, constraints, and queries. -
Design auth and access control
A backend's endpoints are reachable, so decide how callers prove who they are and what each may do — authentication, roles, and permissions — and review the design for gaps before the endpoints exist to exploit.
Outcome An access-control model designed and reviewed before build.
Build & Refine
Build, test, secure, and make it production-ready.
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Review the code as you build
As endpoints land, review them against your conventions with severity-ranked findings — so the backend doesn't accumulate the debt that makes the next version 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 endpoints without breaking the ones clients already use.
Outcome Tests that catch real regressions in the API. -
Run a security review
A backend is the exposed surface and the data behind it. Review the auth and input paths the way an attacker would, and back the findings with tests, before it's reachable.
Outcome Exposed paths reviewed for vulnerabilities and guarded by tests.
Ship & Validate
Ship with confidence and validate results.
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Ship it to production
Cross the gap between tested and live — assess release readiness, plan the deploy and its rollback, and set up monitoring — so the backend goes into production deliberately, not hopefully.
Outcome The backend shipped with a rollback path and monitoring.
Expected outcome
An API backend designed before it was built and built with discipline — requirements pinned, architecture and contracts decided, the data model deliberate, and the code reviewed, tested, security-checked, and shipped with a rollback — a service clients can build against instead of a set of endpoints that hardened by accident.
Best for
- Developers building a backend that other clients will depend on
- Getting the expensive-to-reverse parts right first — contract, data model, auth
- Shipping an API reviewed, tested, and secured, not just running locally
Not for
- A frontend-only project or a static site
- A throwaway script with no consumers
FAQ
Does this include the frontend?
No — it is backend-first: requirements, architecture, API contract, data model, auth, and deployment. Pair it with a website or app project for the frontend.
Why design the contract and data model before coding?
Because they are the expensive things to reverse. The journey front-loads architecture, API contract, and data model so the code you write later is not fighting a bad foundation.
Is security part of this?
Yes — access control is a design stage and a security review is part of the build, because an API with clients on it cannot bolt those on afterward.
How do I build an API backend with AI?
Build it in the order that keeps a backend trustworthy: pin the requirements, decide the architecture, then design the API contract, data model, and access control before writing code, and build it reviewed, tested, security-checked, and shipped with a rollback. Each of the nine stages links to a NewPrompt workflow you run in your own AI tools.
Can I use this blueprint with Node, .NET, Python, or another stack?
Yes, the blueprint is stack-neutral. It designs the architecture, API contract, data model, and auth as decisions, not code, so the nine stages apply whether you build on Node, .NET, Python, Go, or anything else. You pick the framework and write the endpoints; the contract and schema stages shape what you build.
Does NewPrompt write and deploy the backend for me?
No. NewPrompt does not write or deploy the backend for you. The ship stage helps you assess release readiness, plan the deploy and its rollback, and set up monitoring, but you write the endpoints, run the deployment, and make the go-live call in your own environment. AI assists the plan; the release is yours.
Workflows in this project
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 Auth & Identity Workflow
Design access control before you build it, not after a breach — choose the authentication approach, model the roles and permissions, review the design for gaps, then document the identity model.
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 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.
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
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
Security Engineer Role Prompt
Make AI act as a security engineer — threat modeling, secure design, risk assessment, and defensive recommendations — instead of a generic assistant.
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
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
RBAC Design Prompt
Design a role model that scales — roles derived from real permissions, the role-permission mapping, and the inheritance and edge cases before you hardcode 'isAdmin'.
Resource
Permission Matrix Prompt
Make access auditable — a roles-by-actions matrix that shows exactly who can do what, exposes the gaps and over-grants, and becomes the spec the code is checked against.
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
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Guides for this project
Guide
How to Design a Database Schema With AI
Four sentences of requirements go in, a confident schema comes out — and it quietly decided three things nobody decided. Here's how to turn real domain rules and access patterns into a reviewable database schema, with the modeling gaps named as open questions instead of guessed.
Guide
How to Review an API Design With AI Before You Ship It
Code you can refactor on a quiet afternoon. A field you shipped is somebody else's dependency — you don't fix it, you version it. What an hour of contract review buys: method semantics, a truthful error model, retries, authorization, and compatibility you didn't mean to promise.
Guide
How to Design an Access-Control Model (RBAC) With AI
Both of them logged in successfully. One of them is looking at a ticket from another company's workspace. Here's how to turn your real actors, resources and actions into a reviewable RBAC model and a permission matrix where every cell says allow, deny, or on what condition.
Ways to Use This Project Path
Practical project ideas you can build from this base project path — each opens in the Project Advisor.
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