Prompt Design Patterns: From Cognitive Framework to Practical System

Breaking through traditional prompt engineering limitations to build comprehensive intelligent interaction systems

TL;DR

Deepractice Prompt Design Patterns provide ten core prompt types that form a complete work loop: Define → Plan → Execute → Verify → Transfer. These patterns, inspired by software engineering design patterns, offer reusable templates for AI interaction design.

Introduction: From Theory to Practice

In our previous article on the Cognitive Prompt Paradigm, we introduced a seven-dimensional theoretical framework. This article reveals the design pattern improvements and details all ten core prompt types.

We call these "Prompt Design Patterns" following software engineering's design pattern concept. Just as Java Design Patterns provide reusable solution templates for developers, Deepractice Prompt Design Patterns provide systematic, reusable paradigms for AI interaction designers.

Limitations of the Initial Framework

Three Key Limitations

Limitation	Description
Abstract Concepts	Provided macro perspective but lacked implementation guidance
Imprecise Dimensions	Some dimensions had conceptual overlap and fuzzy boundaries
Insufficient Systematization	Lacked organic connections and coordination between dimensions

Four Breakthroughs in the New Patterns

Breakthrough	Description
Concrete Specifications	Each prompt type includes detailed design guidelines
Organic System	Establishes "Define → Plan → Execute → Verify → Transfer" complete loop
Dynamic Capabilities	Introduces TMP, TVP, and KTP—three new prompt types
Practical Application	Provides rich scenarios and examples

The Ten Core Prompt Types

1. Role Responsibility Prompt (RRP)

Purpose: Define AI's professional identity and behavioral boundaries.

Eight Dimensions: Role identity, professional domain, communication style, core responsibilities, behavioral guidelines, capability boundaries, interaction mode, evaluation standards.

2. Protocol Description Prompt (PDP)

Purpose: Standardized contract for AI interaction.

Components: Input specification, output specification, data contract, interaction patterns, exception protocols.

3. Execute Specification Prompt (ESP)

Purpose: Define specific methods and quality standards for task completion.

Components: Processing flow, reasoning methods, execution order, quality standards, edge case handling.

4. Task Management Prompt (TMP)

Purpose: Guide AI in decomposing, scheduling, and monitoring complex objectives.

Components: Goal decomposition, resource planning, execution scheduling, progress monitoring, risk management.

5. Test Validation Prompt (TVP)

Purpose: Define AI's self-validation and quality control for task outcomes.

Components: Validation standards, testing methods, edge cases, verification flow, defect handling.

6. Knowledge Transfer Prompt (KTP)

Purpose: Build knowledge preservation and transfer mechanisms across sessions and environments.

Components: Knowledge extraction, format standardization, context preservation, handoff protocols.

7. Context Awareness Prompt (CAP)

Purpose: Define multi-level environmental information that AI should recognize and extract.

Components: Context recognition, environment adaptation, user state perception, historical continuity.

8. Reference Prompt (RP)

Purpose: Provide domain-specific professional materials and structured information.

Components: Knowledge base content, reference organization, terminology definitions, case libraries.

9. Collaboration Workflow Prompt (CWP)

Purpose: Build collaboration methods and process specifications between AI and users or system components.

Components: Role definitions, interaction protocols, workflow processes, state management.

10. Evolution Adaptation Prompt (EAP)

Purpose: Guide AI self-adjustment and continuous optimization based on feedback and experience.

Components: Evolution mechanisms, adaptation standards, learning strategies, performance evaluation.

The Core Work Loop

The ten prompt types form a systematic work loop:

RRP (Role Definition) → Establish AI's professional identity and scope
       ↓
TMP (Task Planning) → Decompose complex goals into manageable tasks
       ↓
ESP (Execution Methods) → Systematically execute each specific task
       ↓
CAP (Context Awareness) → Recognize and adapt to execution environment
       ↓
TVP (Result Validation) → Verify output quality and discover issues
       ↓
KTP (Knowledge Transfer) → Save experience for next work cycle

Scenario-Based Combination Patterns

Pattern	Prompt Combination	Use Case
Expert Consulting	RRP + RP + ESP + CAP	Professional advisory services
Project Management	RRP + TMP + CWP + CAP	Complex project coordination
Data Analysis	RRP + PDP + ESP + TVP	Structured analytical work
Creative Collaboration	RRP + CAP + CWP + EAP	Iterative creative processes

Practical Example: Expert Consulting Pattern

## Role Responsibility (RRP)
You are a senior financial analyst specializing in emerging markets...

## Reference (RP)
Base your analysis on these sources:
- IMF World Economic Outlook 2024
- Regional market reports from Bloomberg...

## Execute Specification (ESP)
Follow this analysis framework:
1. Macro environment assessment (PESTEL)
2. Industry competitive analysis (Porter's Five Forces)
3. Risk-reward evaluation matrix...

## Context Awareness (CAP)
Adapt recommendations based on:
- Client's risk tolerance level
- Investment time horizon
- Portfolio concentration limits...

Key Benefits

Benefit	Description
Systematic	Complete loop from definition to transfer
Reusable	Patterns applicable across domains
Quality-Focused	Built-in validation and verification
Evolvable	Continuous improvement through feedback
Collaborative	Clear handoff and knowledge transfer

Conclusion

Deepractice Prompt Design Patterns mark the evolution of prompt engineering from "craft" to "engineering discipline." Through systematic application of ten core prompt types, we can build complex, efficient, continuously evolving AI collaboration systems that truly unlock the potential of large language models.

This isn't just better prompting—it's a new paradigm for human-AI collaboration.

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