What is Context Engineering?
Context engineering is the practice of systematically designing and managing the information environment around AI interactions to optimize performance and reliability.
Simply put: Context engineering determines what information the AI has access to and how that information is structured when processing your prompt.
While prompt engineering focuses on crafting the perfect instructions, context engineering focuses on:
- Information Retrieval: Pulling relevant data from databases, documents, or knowledge bases
- Memory Management: Maintaining conversation history and user preferences
- Tool Integration: Connecting AI to external APIs, calculators, or search engines
- Data Formatting: Structuring information in ways the AI can best understand and use
- Context Filtering: Ensuring only relevant, high-quality information reaches the model
Think of it like preparing a briefing for a consultant: prompt engineering is writing clear questions, while context engineering is gathering all the relevant files, data, and tools they'll need to give you the best answer.
Real-World Context Engineering Examples
Here's how context engineering actually injects data into prompts:
👥 AI Recruitment Assistant
1. Base Prompt (Prompt Engineering):
2. Context Retrieved (Context Engineering):
CANDIDATE: Sarah Chen - 5yrs Python, Django, AWS experience
SKILLS_MATCH: 85% technical fit, 92% cultural alignment
SALARY_DATA: Market rate $95-115k for this role
INTERVIEW_HISTORY: Passed initial screening, tech round pending
3. Final Prompt AI Sees:
JOB: Senior Python Developer, $90-120k, Remote OK
CANDIDATE: Sarah Chen - 5yrs Python, Django, AWS experience
SKILLS_MATCH: 85% technical fit, 92% cultural alignment
SALARY_DATA: Market rate $95-115k for this role
INTERVIEW_HISTORY: Passed initial screening, tech round pending
Should we proceed with this candidate?"
Result: AI can make data-driven recommendations instead of generic advice.
📋 HR Policy Assistant
1. Base Prompt (Prompt Engineering):
2. Context Retrieved (Context Engineering):
QUERY: "Can I take 3 weeks annual leave in December?"
POLICY: Max 2 weeks consecutive leave, blackout Dec 15-31
LEAVE_BALANCE: 18 days available, 5 days pending approval
MANAGER_OVERRIDE: Available for senior staff with 2 weeks notice
3. Final Prompt AI Sees:
EMPLOYEE: John Smith, Engineering Manager, Sydney office
QUERY: "Can I take 3 weeks annual leave in December?"
POLICY: Max 2 weeks consecutive leave, blackout Dec 15-31
LEAVE_BALANCE: 18 days available, 5 days pending approval
MANAGER_OVERRIDE: Available for senior staff with 2 weeks notice
What should I tell this employee?"
Result: AI provides policy-compliant advice using employee-specific data and current regulations.
💰 Payroll Processing Assistant
1. Base Prompt (Prompt Engineering):
2. Context Retrieved (Context Engineering):
HOURS: 40 regular + 8 overtime hours
RATE: $28/hour regular, $42/hour overtime
DEDUCTIONS: Tax $187, Super $89, Health Insurance $45
YTD_TOTALS: Gross $31,200, Tax $4,680, Super $2,808
3. Final Prompt AI Sees:
EMPLOYEE: Lisa Wang, Marketing Specialist
HOURS: 40 regular + 8 overtime hours
RATE: $28/hour regular, $42/hour overtime
DEDUCTIONS: Tax $187, Super $89, Health Insurance $45
YTD_TOTALS: Gross $31,200, Tax $4,680, Super $2,808
Calculate this pay period and verify all amounts are correct."
Result: AI processes payroll with real employee data and current tax calculations.
As Tobi Lütke (CEO of Shopify) noted: "I really like the term 'context engineering' over 'prompt engineering.' It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM." This concept has been further endorsed by AI researcher Andrej Karpathy, who has championed the shift toward context engineering as a fundamental skill.
Different Levels, Same Goal
Prompt engineering fine-tunes the exact words you give an AI model.
Context engineering manages the information environment where that prompt operates.
You still need a clear, crisp prompt to start the conversation — context engineering ensures it has the right information to work with!
The Prompt Is Your Contract
A prompt is like a handshake agreement: it tells the model how to behave. Without it, context is just noise.
- Defines format, tone, and constraints
- Anchors the AI's response, no matter how much context is added
Context Depends on Prompts
Even the best retrieval pipelines start with a system prompt — that's prompt engineering at work!
- System messages
- Retrieved documents
- Conversation history
Real-World Workflows Mix Both
Teams prototype prompts, design context, test end-to-end, and iterate — together.
- Draft prompts
- Build retrieval logic
- Integrate and test
- Refine wording and data
When Context Engineering Is Overkill
For simple rewriting or one-off text tasks, a clever prompt often does the trick — adding complex context may be unnecessary.
Bottom Line
Prompt engineering and context engineering are partners, not competitors. Together, they make AI powerful and predictable.
For more info, get in touch with Joey in the EmTech team!