What is Context Engineering?
Context Engineering is the discipline of designing and optimizing the information you provide to AI systems. While prompt engineering focuses on how you ask, context engineering focuses on what information you include.
💡 The Key Insight: AI models can only work with what's in their context window. The quality of outputs is directly proportional to the quality of context provided.
The Context Engineering Framework
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1. Information Retrieval
Pull relevant documents, data, and knowledge
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2. Context Selection
Choose what's most relevant for the task
📐
3. Context Structuring
Organize information for optimal processing
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4. Context Compression
Maximize information density
Key Principles of Context Engineering
🎯 Principle 1: Relevance Over Volume
More context isn't always better. Irrelevant information dilutes attention and can confuse the model.
❌ Bad Context
"Here's our entire company wiki (50 pages)... Now answer this specific question about vacation policy."
✅ Good Context
"Here's the relevant section from our HR policy: [specific paragraph]. Based on this, answer..."
🏗️ Principle 2: Structure Your Context
Well-organized context helps the model locate and use information efficiently.
# CONTEXT
## User Profile
- Name: Sarah Chen
- Role: Marketing Manager
- Experience: 5 years
## Current Task
Writing Q4 marketing strategy
## Available Data
- Q3 performance metrics: [data]
- Budget constraints: $50,000
- Target audience research: [summary]
# INSTRUCTION
Based on the above context, create a Q4 marketing plan.
⏱️ Principle 3: Temporal Context Matters
Include time-relevant information. AI models have knowledge cutoffs and don't know "current" events.
Always include:
- Current date when relevant
- Time-sensitive data (prices, stats, regulations)
- Recent updates the model wouldn't know
🔄 Principle 4: Dynamic Context Loading
For complex tasks, load context progressively rather than all at once.
Multi-Stage Context Loading:
Stage 1: Load problem overview → Get initial analysis
Stage 2: Load relevant details based on Stage 1 → Deeper investigation
Stage 3: Load specific data points → Final solution
Context Engineering Patterns
Dynamically retrieve relevant documents and inject them into the context.
# Retrieved Documents (based on user query)
[Document 1: Product specifications...]
[Document 2: Customer FAQ...]
# User Query
"What's the battery life of the Pro model?"
# Instruction
Answer using ONLY the information in the retrieved documents.
Maintain conversation history and user preferences as persistent context.
# Long-term Memory
- User prefers concise answers
- User is an expert in Python
- Previous topics: API design, testing
# Short-term Memory (Last 3 exchanges)
[Previous conversation...]
# Current Message
"How should I structure the tests?"
Define a rich persona with knowledge, skills, and constraints.
# Assistant Persona
You are Alex, a senior DevOps engineer at a Fortune 500 company.
## Your Knowledge
- 10 years of experience with AWS, Kubernetes, Docker
- Expert in CI/CD pipelines
- Familiar with compliance requirements (SOC2, HIPAA)
## Your Communication Style
- Direct and technical
- Prioritizes security and scalability
- Asks clarifying questions before making recommendations
## Your Constraints
- Never suggest deprecated practices
- Always consider cost implications
- Recommend monitoring for every solution
Context Window Optimization
1
Summarize when possible
Replace long documents with concise summaries that preserve key information.
2
Use hierarchical context
Overview first, details on demand. Let the model ask for more if needed.
3
Prioritize recent context
Place most important/recent information closer to the instruction (end of context).
4
Remove redundancy
Don't repeat information. Each piece of context should add unique value.
Practical Example: Building a Customer Support Agent
# SYSTEM CONTEXT
## Agent Identity
You are a customer support agent for TechCorp, a SaaS company.
## Knowledge Base (dynamically loaded)
### Product Info
- Product: CloudSync Pro
- Version: 4.2.1 (released: Nov 2024)
- Pricing: $29/mo (Basic), $79/mo (Pro), $199/mo (Enterprise)
### Common Issues & Solutions
1. Sync errors → Clear cache, check internet, restart app
2. Login issues → Password reset, check 2FA, verify email
3. Billing questions → Redirect to billing@techcorp.com
### Policies
- Refund window: 30 days
- SLA: 99.9% uptime
- Support hours: 24/7 for Pro/Enterprise
## Current Customer Context
- Name: John Smith
- Plan: Pro ($79/mo)
- Account age: 8 months
- Recent tickets: 2 (both resolved)
- Current issue: Sync not working
## Conversation Guidelines
1. Be empathetic and professional
2. Gather diagnostic info before troubleshooting
3. Escalate complex technical issues to Tier 2
4. Always confirm resolution before closing
# INSTRUCTION
Respond to the customer's message while following all guidelines above.
🔑 Key Takeaway: Context Engineering is about providing the AI with the right information, structured correctly, at the right time. Master this, and you'll unlock the full potential of AI systems.