Context + Workflow Design - Prompt Engineering Guide

🏗️ System Architecture for Prompt Engineering

Learn how to design scalable, maintainable prompt engineering systems that handle context management, workflow orchestration, and production deployment.

🎭 System vs User Prompts

Understanding the difference between system-level instructions and user-specific requests is crucial for building robust AI applications.

🔧 System Prompts

Foundation instructions that define the AI's behavior, capabilities, and constraints.

System: You are an expert marketing consultant with 15 years of experience in B2B SaaS. You specialize in customer acquisition strategies and always provide actionable, data-driven advice. Never make up statistics or facts.

System: You are a helpful coding assistant. Always write clean, well-documented code. Explain your reasoning and suggest improvements. Follow best practices for the specified programming language.

👤 User Prompts

Specific requests or questions that users send to the AI system.

User: Create a marketing strategy for our new product launch targeting enterprise customers in the healthcare sector.

User: Write a Python function to calculate customer lifetime value based on purchase history and retention data.

🔄 Prompt Engineering Workflow Pipeline

See how prompts flow through a complete system from input to output:

1. System Prompt

Define AI behavior, constraints, and capabilities

2. Context Management

Gather and organize relevant information

3. User Input

Process specific user requests

4. Prompt Assembly

Combine system, context, and user input

5. AI Processing

Generate response using assembled prompt

6. Response Validation

Check quality, safety, and relevance

7. User Delivery

Present final response to user

📊 Context Window Management

Efficiently manage limited context windows to maximize AI performance and cost-effectiveness.

🎯 Context Window Visualization

See how different content types consume your context budget:

System Prompt: 0%

User Context: 0%

Conversation History: 0%

Examples: 0%

Smart Context Management Techniques

🎯 Priority-Based Selection

Keep most relevant information, discard less important context

🔄 Rolling Window

Maintain recent conversation history, archive older interactions

📝 Summarization

Condense long conversations into key points

🏷️ Semantic Search

Retrieve only contextually relevant information

🔗 Delimiters & Templates

Use structured templates and clear delimiters to create consistent, maintainable prompts.

📋 Basic Template Structure

System: [AI role and behavior] Context: [Relevant background information] Instructions: [Specific task requirements] Format: [Desired output structure] Constraints: [Limitations and rules]

🎯 Advanced Template with Delimiters

🔄 Iterative Template

💡 Best Practices for Workflow Design

Follow these proven strategies for building robust prompt engineering systems:

🔒 Security First

Always validate and sanitize user inputs. Implement prompt injection protection and output filtering.

📊 Monitoring & Logging

Track prompt performance, user satisfaction, and system behavior for continuous improvement.

🔄 Version Control

Maintain prompt versions and track changes. Use A/B testing for prompt optimization.

⚡ Performance Optimization

Minimize context usage, implement caching, and optimize for response time and cost.

🛡️ Error Handling

Gracefully handle AI failures, timeouts, and unexpected responses with fallback mechanisms.

📱 User Experience

Design intuitive interfaces that guide users to create effective prompts without technical knowledge.

📚 Complete Guide Navigation

Access all sections of the comprehensive prompt engineering guide:

⚙️ Context + Workflow Design