Methodology

The AI Development Framework Guide

A complete methodology for structuring software projects so AI assistants produce consistent, high-quality code from session one.

The core problem

Most people use AI for coding the same way: open a chat, describe what they want, and hope for the best. This works for small scripts, but falls apart on real projects.

No memory

The AI doesn't know your conventions, your architecture, or what you've already built. Every session starts from zero.

Inconsistent output

Without guidance, AI invents its own patterns — different patterns every session. Your codebase becomes a patchwork.

Wasted iterations

You spend more time correcting the AI than coding. 15 rounds of "that's not what I meant" before you get something usable.

The solution: a project framework

A project framework is a set of structured documents that give AI everything it needs to do excellent work. Instead of explaining your project from scratch every time, you hand the AI a set of files that describe your project's identity, conventions, features, and current task.

The five pillars

Every framework is built on the same five document types.

01

Project Identity

A single master file that describes what your project is, what tech stack it uses, and how it's structured. This is the first file every AI session reads.

Keep it under 3,000 words — concise enough to fit in context, detailed enough to prevent misunderstandings.

02

Conventions

The decisions that keep your codebase consistent: how components are organized, how data is fetched, how errors are handled, how files are named.

Without conventions, AI will invent its own patterns — different patterns every session.

03

Features

Each feature gets its own file describing what it does, its business rules, and which database tables it touches. Features describe outcomes, not implementation steps.

"Users can book appointments" — not "Create a form with date picker, then call the API."

04

Tasks

Features broken into focused tasks — each small enough for a single AI session. Every task has a Definition of Done that tells the AI exactly what finished looks like.

Tasks own specific files and directories, preventing AI sessions from stepping on each other.

05

Context Window Management

Don't dump everything into the AI's context. Give it the master file + conventions + the specific feature + the specific task. Focused context produces dramatically better results.

More context ≠ better results. The right context = better results.

The workflow

Five steps from planning to building. Repeat for every task.

Step 1

Create your framework

Walk through the guided wizard to define your project's identity, architecture, conventions, features, and tasks.

Step 2

Copy the context starter

For each coding session, grab the context window starter prompt — it tells the AI exactly which files to read.

Step 3

Paste into any AI tool

Works with Claude, ChatGPT, Cursor, Windsurf, Copilot — any AI coding assistant. The framework is tool-agnostic.

Step 4

Review against the Definition of Done

Each task has a clear checklist. Review the AI's output against it. If it passes, move on. If not, iterate with context intact.

Step 5

Move to the next task

Start a fresh session with the next task's context. The AI already knows your project — it just needs the new assignment.

The key insight

AI doesn't need your entire codebase. It needs the right slice of context for the task at hand. A project framework gives every AI session exactly what it needs — no more, no less.

The result: consistent code, fewer iterations, and AI that actually understands what you're building.

Ready to build with clarity?

It takes about 10 minutes to set up a framework. It saves hours of wasted AI sessions.

Start Your Project