AI-DLC Project Documents / Overview

How AI-DLC Works in This App

How AI-DLC works, explained in plain language

How AI-DLC Works in This App

Purpose

This document explains the AI-Driven Development Life Cycle as it was applied to this application.

It is intended for readers who want to understand the methodology in plain language before reading the lower-level artifacts.

What AI-DLC Is

AI-DLC is a structured way of building software with AI assistance.

Instead of jumping directly from an idea to generated code, the work is broken into explicit lifecycle phases, planned decisions, review checkpoints, and traceable artifacts.

The core idea is simple:

  • capture intent clearly
  • ask questions when requirements are ambiguous
  • produce planning artifacts before implementation
  • separate design from implementation
  • preserve a written trail of decisions and progress

Why This Matters

Without a structured process, AI-generated projects can become difficult to understand, review, or maintain.

AI-DLC makes the work easier to trust because it leaves behind:

  • requirements
  • plans
  • design records
  • audit history
  • state tracking
  • implementation context

That means people who were not part of the original AI session can still understand what happened.

The Three Lifecycle Phases

1. Inception

The Inception phase focuses on what should be built and why.

In this project, that includes:

  • understanding the request
  • checking whether the project is new or existing
  • collecting and clarifying requirements
  • deciding which workflow stages are needed
  • designing the application at a high level
  • defining units of work where needed

The main output of this phase is a shared understanding of the intended solution.

2. Construction

The Construction phase focuses on how the solution should be built.

In this project, that includes:

  • functional design
  • non-functional requirement analysis
  • code-generation planning
  • implementation preparation
  • build and validation guidance

The main output of this phase is an implementation-ready blueprint plus the code and technical artifacts that follow from it.

3. Operations

The Operations phase focuses on how the resulting work is published, understood, and maintained over time.

In the original AI-DLC rule set this phase is lighter than the earlier phases, so this repository supplements it with public-facing documentation and discoverability.

The main output of this phase is visibility: readers can understand the work after the original AI session is gone.

How the Workflow Actually Proceeds

AI-DLC is adaptive rather than rigid.

That means the workflow does not always execute every possible stage.

Instead, it evaluates:

  • project type
  • complexity
  • clarity of the request
  • risk level
  • architectural impact
  • documentation needs

For simple work, fewer stages may be needed.

For more complex work, the workflow expands and creates more artifacts.

How Questions and Clarifications Work

A key part of AI-DLC is that unclear requirements are not silently guessed.

Instead:

  1. questions are generated
  2. answers are captured in documents
  3. ambiguity is reduced before implementation
  4. downstream artifacts use those answers as context

This improves traceability and reduces accidental drift between intent and implementation.

How Planning Works

Before major implementation begins, AI-DLC produces planning artifacts.

These plans help answer:

  • what stages should run
  • what should be skipped
  • why each stage matters
  • what outputs should be created
  • what success looks like

This is important because it turns AI work from a black box into a reviewable process.

How Design and Code Relate

AI-DLC separates design intent from implementation.

That means:

  • design artifacts explain the intended architecture and rationale
  • source code shows the realized implementation
  • audit and state files record how the workflow progressed

When differences appear between a planning document and the code, the documents still retain value because they show the reasoning path that led to the result.

What Artifacts This App Exposes Publicly

This application makes the AI-DLC project documents public so readers can inspect the methodology directly.

The public artifact set includes:

  • workflow state tracking
  • audit history
  • requirements
  • execution planning
  • application design documents
  • construction plans

These artifacts are not just supporting notes. They are part of the product story of how the application was created.

Recommended Reading Order

If you want to understand this project end to end, read in this order:

  1. aidlc-docs/README.md
  2. aidlc-docs/how-aidlc-works.md
  3. aidlc-docs/aidlc-state.md
  4. aidlc-docs/inception/requirements/requirements.md
  5. aidlc-docs/inception/plans/execution-plan.md
  6. aidlc-docs/inception/application-design/application-design.md
  7. aidlc-docs/audit.md

What This Gives Users

For users, AI-DLC makes the app more transparent.

For collaborators, it makes the app easier to review and continue.

For maintainers, it makes the reasoning behind the project discoverable long after the original creation session has ended.