AIPD: AI-Driven Document-Structured Software Development Workflow

Core Overview of AIPD

AIPD (AI-Powered Development) is a document-structured software development workflow centered on structured documents, with the core concept of "Documents as Contracts", allowing AI Agents to lead project planning, design, and implementation. This workflow aims to solve issues like context loss and requirement deviations in existing AI programming tools, enabling humans to focus on document review and high-level thinking.

Project Basic Information

• Original Author/Maintainer: KarnaughK
• Source Platform: github
• Original Link: https://github.com/KarnaughK/AIPD
• Release Time: 2026-06-06T16:15:46Z

With the improvement of large language model capabilities, AI-assisted programming has evolved from code completion to complex task processing (e.g., GitHub Copilot, Devin). However, the "human-machine dialogue" mode of existing tools has problems like context loss, requirement understanding deviations, and poor code consistency in complex projects. AIPD proposes a new paradigm: centered on structured documents, AI Agents become the main drivers of the project.

Core Concepts

1. Documents are the only source of truth
2. AI Agents execute autonomously according to documents
3. Humans focus on document-level review
4. Document-driven iteration

Document Hierarchy

• PRD (Product Requirements Document): Defines "what to do"
• TDD (Technical Design Document): Defines "how to do it"
• ADR (Architecture Decision Record): Records key choices
• Task Specs: Specific implementation requirements

Agent Workflow

Planning → Execution → Review → Iteration

Standardization of Document Format

Use Markdown with YAML metadata, including status tags, traceable IDs, etc.

Agent Context Management

Hierarchical retrieval, dependency graph construction, incremental updates

Human-Machine Collaboration Interface

Document editor, Agent monitoring panel, review workflow, bidirectional change tracing

Advantages

Scalability, maintainability, consistency, traceability

Challenges

Document writing cost, Agent understanding ability, flexibility limitations, lack of tool ecosystem

Dimension	Traditional Development	Copilot Mode	AIPD Mode
Requirement Carrier	Verbal/Tickets	Dialogue History	Structured Documents
AI Role	None/Tool	Assistant	Executor
Human Role	Full Implementation	Lead + Review	Planning + Acceptance
Context Management	Human Memory	Dialogue Window	Documents + Knowledge Graph
Maintainability	Depends on Code Comments	Depends on Dialogue Records	Documents as the Source

Future directions: Automatic document generation, intelligent maintenance, multi-Agent collaboration, domain templates

AIPD positioning: Does not replace humans; frees humans from detailed work, allowing them to focus on product and architecture design