Short Intro
In a recent Technology Innovation Summit hosted by SaskToday.ca, digital strategist Shelly Palmer challenged the idea that prompt engineering is the apex of generative AI. Instead, he introduced “context engineering” as the next big leap. By weaving in relevant data, user history, and environmental cues, Palmer argued, we can build AI applications that feel more natural, accurate, and valuable.
Article
When generative AI first caught the world’s attention, prompt engineering quickly became its core skill. Professionals learned to craft precise questions, add system instructions, and insert “few-shot” examples to guide models like ChatGPT. Early successes in content creation, code generation, and market research showed that clear, well-structured prompts could yield surprisingly good results. Yet as AI tools spread across industries, experts began to see the limits of prompt engineering alone.
At the summit, Shelly Palmer framed those limits this way: “Prompt engineering was like the early days of search engine optimization. You could tweak keywords, and the algorithm would dance to your tune. But real power comes when you provide the full picture—context—for the AI to interpret.” His talk outlined how moving from prompt to context can make AI responses richer, more precise, and better aligned with user needs.
Defining Context Engineering
Palmer defines context engineering as the practice of gathering, curating, and presenting all relevant information around a user’s request. This includes:
• Static data: product specs, code repositories, legal documents.
• Dynamic data: real-time KPIs, market trends, live sensors.
• User history: past chats, purchase records, browsing patterns.
• Environmental cues: location, device type, time of day.
By supplying a broader information map, developers let AI models draw deeper connections. Rather than just responding to a prompt, the model uses context to infer user goals, anticipate needs, and flag potential issues.
The Five-Step Framework
Palmer shared a simple framework—dubbed the Five Cs of Context Engineering—to turn context theory into practice:
1. Capture: Collect all relevant data sources. Set up APIs to feed in company reports, live dashboards, or IoT signals.
2. Curate: Filter and organize. Keep the data accurate, recent, and compliant with privacy rules.
3. Compress: Use embeddings and vector databases to distill large datasets into manageable representations.
4. Compute: Combine those embeddings with prompt templates and system instructions in real time.
5. Calibrate: Test outputs, adjust weights, and iterate until the model delivers reliable results.
This loop ensures that context isn’t a one-time add-on but a living layer that adapts as business conditions and user needs change.
Real-World Examples
Palmer illustrated context engineering with two case studies. At a midsize e-commerce brand, a chatbot evolved from answering basic FAQs to offering personalized product bundles. By tapping purchase history, browsing time per page, and live inventory levels, the AI suggested complementary items that were in stock. The result: a 15 percent boost in average order value.
In healthcare, a telemedicine provider linked patient records, current symptoms, regional health advisories, and medication databases. Doctors using the system saw context-powered summaries that highlighted drug interactions and recent test data. They cut patient call time by 30 percent while improving diagnostic accuracy.
Ethical and Technical Hurdles
Context engineering adds complexity. More data means higher security risks and tougher privacy challenges. Palmer stressed the need for strong encryption, strict access controls, and transparency about how personal data is used. He also warned against “data dumping”—throwing everything into a model without filtering for relevance or bias.
On the technical side, organizations must invest in infrastructure such as vector databases, real-time APIs, and orchestration tools. “Building context layers isn’t plug-and-play,” Palmer said. “You need a clear architecture, skilled engineers, and ongoing governance to keep your system healthy.”
The Future of Intelligent Systems
Looking ahead, Palmer predicted that AI platforms will offer built-in context modules. Developers will drag and drop connectors for CRM systems, ERP data, and social feeds. No-code interfaces will let project managers map data flows without writing a line of SQL. Meanwhile, open standards for context sharing will emerge, letting small startups tap the same rich data streams that big tech uses.
In this vision, context engineering becomes a democratized discipline. Marketing teams, customer success managers, and product designers will all play a role in shaping the data that drives AI. The result? Tools that understand not just what you ask, but why you ask it.
3 Takeaways
• Context trumps prompts. Supplying richer data leads to more accurate and relevant AI responses.
• Five Cs framework. Capture, Curate, Compress, Compute, and Calibrate guide practical implementation.
• Ethical guardrails matter. Strong privacy, security, and bias controls are essential in context engineering.
3-Question FAQ
Q: What exactly is context engineering?
A: It’s the practice of collecting and organizing all relevant data—static, dynamic, user-specific, and environmental—so AI can deliver more informed responses.
Q: How can businesses start with context engineering?
A: Begin by mapping your key data sources. Then set up a lightweight proof of concept that links one context type—like user history—to a generative AI model.
Q: Do you need coding skills to do this?
A: Basic data integration often requires coding or low-code tools. But emerging no-code platforms are making context orchestration accessible to non-developers.
Call to Action
Ready to move beyond prompts and build AI that truly understands your world? Subscribe to our newsletter for more insights on context engineering. Check out Shelly Palmer’s website for in-depth guides, and follow him on social media for the latest updates in AI strategy and innovation.
