AI-DLC Methodology for This App

This page explains how the Snake game project uses AI-DLC, where the documentation lives, and how the methodology connects requirements, design, implementation, review, and public transparency.

Project: Snake AI-DLC Approach: AI-Driven Development Life Cycle Public artifacts: /aidlc-docs/ Public rules: /aidlc-rules/

Open the game Read AI-DLC documents in browser Read AI-DLC rules in browser

What AI-DLC means here

AI-DLC stands for AI-Driven Development Life Cycle. It is an adaptive methodology that uses structured phases, decision records, planning artifacts, and implementation traceability to help teams understand both what was built and why it was built that way.

In this project, AI-DLC was not treated as invisible background tooling. The method itself is made visible through requirements, planning records, design artifacts, state tracking, and audit history so readers can inspect the project as a traceable process instead of only a final code snapshot.

The public materials are intentionally split into two complementary sets: the generated project documents in `/aidlc-docs/`, and the reusable methodology and workflow rules in `/aidlc-rules/`. Together they show both the outcome of the process and the rule system that guided it.

Inception

Define what to build and why.

Requirements analysis
Workflow planning
Application design
Unit-of-work definition

Construction

Define how to build it.

Functional design
NFR planning
Code-generation planning
Build and test readiness

Operations

Make the work visible, understandable, and maintainable.

Public documentation visibility
Artifact publication
Reader onboarding
Long-term traceability

How the workflow actually works

AI-DLC is adaptive. It does not force every project through the same fixed checklist. Instead, it looks at the kind of request, the complexity of the work, the state of the codebase, and the amount of ambiguity that still exists.

It starts by understanding the request and checking the workspace state.
It asks clarifying questions when the request is unclear.
It produces planning artifacts before major implementation work begins.
It separates design intent from code generation.
It records workflow state and major decisions for later review.
It keeps the resulting artifacts readable to people who were not in the original AI session.

How to read the public project documents

The public `aidlc-docs` set contains the project documents generated through the AI-DLC approach. These are not just notes. They are part of the documented lifecycle of the application.

/aidlc-docs/README.md — starting point for the public project document set
/aidlc-docs/how-aidlc-works.md — plain-language explanation of the methodology in this app
/aidlc-docs/aidlc-state.md — workflow progress and stage tracking
/aidlc-docs/inception/requirements/requirements.md — requirements captured during the project lifecycle
/aidlc-docs/inception/plans/execution-plan.md — stage selection and planning logic
/aidlc-docs/inception/application-design/application-design.md — architecture and design intent
/aidlc-docs/audit.md — audit trail of key AI-DLC interactions

How to read the public AI-DLC rules

The public `aidlc-rules` set contains the reusable methodology rules used to shape the work. This is the rule layer behind the generated documents.

/aidlc-rules/README.md — entry point for the public rule set
/aidlc-rules/aws-aidlc-rules/core-workflow.md — top-level workflow contract
/aidlc-rules/aws-aidlc-rule-details/common/process-overview.md — lifecycle overview
/aidlc-rules/aws-aidlc-rule-details/common/terminology.md — shared terminology and definitions
/aidlc-rules/aws-aidlc-rule-details/inception/workflow-planning.md — planning-stage rules
/aidlc-rules/aws-aidlc-rule-details/construction/code-generation.md — implementation-stage rules

Where the documentation lives

README.md — top-level project entry point
aidlc-docs/README.md — project-specific AI-DLC documentation index
aidlc-docs/how-aidlc-works.md — detailed explanation of the AI-DLC lifecycle in this app
aidlc-rules/README.md — methodology and rule-set overview
/aidlc-docs/ — public project documentation index
/aidlc-rules/ — public methodology and rules index

How to read the artifacts correctly

Some AI-DLC documents capture planning intent from earlier stages. The implementation can evolve after those artifacts are created.

Use this order of precedence when checking the current truth of the system: live app behavior, source code, then AI-DLC planning and design artifacts.

Why these documents are public

They make the creation process understandable to readers outside the original AI session.
They preserve requirements, planning decisions, and design rationale alongside the application.
They help collaborators review not only the result, but also the method used to reach it.
They turn the app into a transparent example of the AI-DLC approach in practice.

The repository includes both implementation files and AI-DLC artifacts so future collaborators can understand the methodology without needing access to the original chat history.