The 4P Theory: A Systematic Framework for AI Engineering

A systematic approach to AI engineering based on Deepractice Prompt Design Patterns

TL;DR

The 4P Theory bridges the gap between abstract prompt patterns and deployed AI products through four progressive stages:

Prompt Pattern → Add Domain →
Domain Prompt → Add Scenario →
Scenario Project → Add Realization →
Realization Product

Each stage builds on the previous, creating a complete value chain for AI engineering.

Why We Need the 4P Theory

The Gap Between Patterns and Products

Prompt design patterns provide systematic methods for crafting prompts, but practitioners face a critical challenge: the unclear transformation path from prompt patterns to actual products.

Specific challenges include:

Challenge	Description
Abstraction Gap	Design patterns are abstract; applying them to specific domains remains unclear
Evolution Missing	No systematic framework for continuous improvement based on feedback
Disconnected Workflow	No clear phase divisions from prompt design to final product

The 4P Theory addresses these challenges by providing a structured transformation methodology.

The 4P Framework

The Four Stages and Transformation Formulas

Prompt Pattern + Domain = Domain Prompt
Domain Prompt + Scenario = Scenario Project
Scenario Project + Realization = Realization Product

Each formula specifies the key element needed to advance to the next stage.

Stage Breakdown

Stage 1: Prompt Pattern (Highest Abstraction)

Definition: Meta-level patterns for constructing prompts—patterns about how to design prompts.

Characteristics:

Domain-agnostic
Cross-industry applicable
Meta-thinking frameworks

Examples: RRP (Role Responsibility Prompt), ESP (Execute Specification Prompt), PDP (Protocol Description Prompt)

Stage 2: Domain Prompt (High Abstraction)

Definition: Prompt patterns combined with domain-specific knowledge to create industry-focused templates.

Characteristics:

Contains domain knowledge without specific scenarios
Industry-applicable across multiple use cases
Follows Prompt Pattern structure

Examples:

Financial Analyst RRP = RRP Pattern + Financial Domain Knowledge
Medical Report PDP = PDP Pattern + Healthcare Domain Knowledge

Stage 3: Scenario Project (Low Abstraction)

Definition: Domain prompts combined with specific business scenarios to form complete project plans.

Characteristics:

Contains concrete scenarios and implementation details
Customized for specific business needs
Directly actionable

Examples:

Bank Customer Churn Prediction AI Project
Hospital Intelligent Diagnosis Assistant Implementation

Stage 4: Realization Product (Zero Abstraction)

Definition: The final deliverable—concrete implementation of AI capabilities meeting business needs.

Characteristics:

Fully concrete implementation
User-facing
Direct business value creation

Examples:

Deployed Banking Customer Service Chatbot
Production Medical Imaging Diagnosis System

Hierarchical Comparison

Level	Stage	Abstraction	Scope	Reusability
L1	Prompt Pattern	Highest	Domain-agnostic	Extremely High
L2	Domain Prompt	High	Specific domain	High
L3	Scenario Project	Low	Specific scenario	Low
L4	Realization Product	None	Concrete implementation	Minimal

The Feedback Loop

The 4P Framework establishes hierarchical feedback cycles:

Prompt Pattern → Domain Prompt → Scenario Project → Realization Product
     ↑               ↑               ↑                    |
     |               |               └────────────────────┘
     |               └──────────────────────────────────────
     └──────────────────────────────────────────────────────
                    Hierarchical Feedback Optimization

Feedback Principles:

Immediate: Realization Product feedback first optimizes its Scenario Project
Aggregated: Multiple scenario experiences elevate to Domain Prompt improvements
Abstracted: Cross-domain patterns refine Prompt Patterns

Practical Application: Financial Industry

Stage 1: Prompt Pattern

Select RRP, ESP, PDP as foundational architecture

Stage 2: Domain Prompt

Financial Analyst RRP (RRP + financial expertise)
Investment Analysis ESP (ESP + investment methodology)
Financial Report PDP (PDP + reporting standards)

Stage 3: Scenario Project

Personal Investment Advisor scenario design
User profiling, analysis workflow, recommendation standards

Stage 4: Realization Product

Deployed Robo-Advisor Platform
Personalized asset allocation and investment recommendations

Cross-Domain Knowledge Transfer

A key 4P value is enabling cross-domain knowledge transfer:

Level	Transfer Capability
Prompt Pattern	Meta-patterns reusable across all domains
Domain Prompt	Different domains can learn from each other
Scenario Project	Similar scenarios applicable across industries

This structured knowledge organization dramatically improves AI engineering efficiency and quality.

Enterprise AI Capability Building

4P Maps to Organizational Development

Organization Building	4P Stage	Corresponding Activity
Meta-capability Definition	Prompt Pattern	Define AI team core methodology
Specialist Team Formation	Domain Prompt	Build domain-specific expert teams
Project Team Assembly	Scenario Project	Design scenario solutions
Execution & Delivery	Realization Product	Transform designs into products

AI Maturity Evolution Path

Foundation: Master basic Prompt Patterns for simple scenarios
Growth: Establish multi-domain Domain Prompt libraries
Maturity: Systematically manage Scenario Projects
Leadership: Build efficient Realization mechanisms
Excellence: Form complete feedback loops across all levels

Conclusion

The Deepractice 4P Theory systematically extends prompt design patterns, filling the gap between design and application. Through clear phase divisions, transformation conditions, and feedback mechanisms, it provides structured AI engineering methodology and systematic enterprise AI capability building pathways.

As AI becomes ubiquitous, mastering this framework will be a key competitive advantage for organizations building efficient AI applications.

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