We’ve spent two years teaching everyone about “prompt engineering,” which has been great. But crafting clever questions represents perhaps five per cent of what makes enterprise AI successful. Now, there’s a new term being added to the buzzword bingo lexicon: “context engineering.” I want to make fun of it, but I really like it.
Context engineering isn’t about the evolution of end-user behaviour; it’s a nice way to describe the components you need to get the most out of the current crop of LLMs and reasoning engines.
What Is Context Engineering?
Andrej Karpathy, a founding member of OpenAI and former Director of AI at Tesla, says: “+1 for ‘context engineering’ over ‘prompt engineering’… In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step.”
AI Systems Thinking
AI systems don’t just store and retrieve; they synthesize and reason across vast, heterogeneous data sources.
Consider a seemingly simple request: “What’s our exposure to the XYZ market given current conditions?”
A traditional system would query predefined reports. A context-engineered AI system would:
- Pull current positions from trading systems
- Analyze recent market movements from external feeds
- Review internal research reports
- Check compliance limits and risk parameters
- Consider historical patterns from similar conditions
- Synthesize insights from news and analyst reports
- Generate a comprehensive analysis with specific recommendations
The difference between these approaches is the difference between a 20-minute manual analysis and a 20-second comprehensive assessment that considers factors your analysts might miss.
Context Engineering in Action
One of our financial services clients recently implemented context engineering for their wealth management division. By connecting market data, client portfolios, regulatory requirements, and relationship history, their advisors now receive AI-generated insights that would have required hours of cross-functional meetings to compile. The result? 40% reduction in prep time and significantly more personalized client strategies.
The Business-Technology Alignment Challenge
The biggest obstacle to effective context engineering isn’t technical, it’s organizational. Business units own the context (data, procedures, expertise) while IT owns the infrastructure. Context engineering requires unprecedented collaboration between these traditionally separate domains.
Business Units Must:
- Identify which context sources matter for their decisions
- Define quality standards for different types of information
- Specify how different contexts relate and interact
- Determine acceptable latency for different use cases
- Establish governance rules for sensitive information
Technology Teams Must:
- Build robust integration architectures
- Ensure real-time data synchronization
- Implement sophisticated access controls
- Optimize for cost and performance
- Maintain system reliability and scalability
Together They Must:
- Map business processes to context requirements
- Design feedback loops for continuous improvement
- Establish metrics for context quality and completeness
- Create governance frameworks for AI decision-making
- Build change management processes for context evolution
Here’s what a context-engineered system architecture might include:
Internal Context Sources:
- Enterprise data warehouses and lakes
- CRM and ERP systems
- Document management platforms
- Internal knowledge bases and wikis
- Email and communication archives
- Proprietary databases
- Historical transaction data
- Policy and procedure documentation
External Context Sources:
- Real-time market data feeds
- Regulatory databases
- Industry intelligence platforms
- News and social media monitoring
- Weather and logistics data
- Competitive intelligence systems
- Third-party APIs
- Public records and filings
Context Processing Layers:
- Data integration and ETL pipelines
- Embedding and vector databases
- Semantic search capabilities
- Entity resolution systems
- Memory management infrastructure
- Privacy and access controls
- Quality assurance mechanisms
- Performance optimization
A Practical Context Engineering Roadmap
Phase 1: Context Inventory – Start by mapping your context landscape. What information do your teams use to make decisions? Where does it live? How current is it? How reliable?
Key deliverable: A comprehensive context map showing all data sources, their owners, update frequencies, and business criticality.
Phase 2: Integration Architecture – Design the technical infrastructure to access and process identified context sources. This includes API development, data pipeline construction, and security framework implementation.
Key deliverable: Technical architecture supporting dynamic context assembly with appropriate governance controls.
Phase 3: Context Orchestration – Build the intelligence layer that determines which context to retrieve for different queries. This involves creating semantic mappings, relevance algorithms, and performance optimization strategies.
Key deliverable: Functioning context orchestration system that dynamically assembles relevant information.
Phase 4: Continuous Optimization – Context engineering isn’t a project, it’s a discipline. Establish processes for monitoring context quality, gathering user feedback, and continuously expanding context sources.
Key deliverable: Operational excellence framework for context engineering.
The Competitive Imperative
Organizations that master context engineering will have AI systems that truly understand their businesses. A well-context-engineered system won’t just answer questions, it will anticipate information needs before they’re articulated, maintain institutional memory across personnel changes, apply your company-specific logic and business rules, respect governance requirements and compliance frameworks, learn from usage patterns to improve over time, and scale seamlessly with business complexity.
This translates directly to measurable outcomes: faster decision-making, reduced operational costs, improved compliance, and the ability to identify opportunities your competitors miss because their AI lacks context.
Said differently, it’s the difference between using off-the-shelf solutions (even really good 3rd-party solutions) and building a business-outcome-oriented AI solution.
But Isn’t This Just System Integration?
Skeptics might argue this is just traditional system integration with an AI wrapper. They’re missing the point. Traditional integration moves data. Context engineering creates understanding. It’s the difference between giving someone a library card and giving them a research assistant who’s read every book and knows exactly where to find what you’re looking for.
Common Pitfalls to Avoid
1. The Data Dump Fallacy Simply connecting AI to all available data sources doesn’t create context, it creates noise. Context engineering requires intelligent curation and relevance filtering.
2. The Silo Trap Building separate context systems for different departments defeats the purpose. Context engineering should create unified intelligence across the enterprise.
3. The Static Context Mistake Business context evolves constantly. Systems must be designed for continuous context updates, not one-time configurations.
4. The Security Afterthought Context engineering amplifies both capabilities and risks. Security and governance must be foundational, not bolted on.
Questions for Your Leadership Team
- Who owns context engineering in your organization? If the answer is unclear, you have an organizational challenge before you have a technical one.
- What percentage of your critical business context is AI-accessible today? Most enterprises discover it’s less than 20%.
- How do you measure context quality? Without metrics, you can’t improve.
- What’s your context refresh strategy? Static context leads to stale AI.
- How does context engineering fit your AI governance framework? This isn’t optional in regulated industries.
The Path Forward
Context engineering represents the maturation of enterprise AI from experimental technology to operational capability. It’s not about teaching employees new ways to interact with AI, it’s about building AI systems that deeply understand your business.
Context engineering leverages existing enterprise capabilities: data management, system integration, governance frameworks. The challenge is orchestrating these capabilities in service of AI rather than traditional applications.
Start by bringing your business and technology leaders together around this question: “What context would transform our AI from a smart assistant into a knowledgeable partner?” The answer will drive your context engineering strategy.
Is “context engineering” another buzzword? Perhaps. But it’s a useful one that captures a critical need: orchestrating your entire information ecosystem to make AI truly intelligent about your business. In an era where competitive advantage comes from decision speed and quality, context isn’t just king… it’s the entire kingdom.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.
About Shelly Palmer
Shelly Palmer is the Professor of Advanced Media in Residence at Syracuse University’s S.I. Newhouse School of Public Communications and CEO of The Palmer Group, a consulting practice that helps Fortune 500 companies with technology, media and marketing. Named LinkedIn’s “Top Voice in Technology,” he covers tech and business for Good Day New York, is a regular commentator on CNN and writes a popular daily business blog. He's a bestselling author, and the creator of the popular, free online course, Generative AI for Execs. Follow @shellypalmer or visit shellypalmer.com.
