Context Engineering: Your New Skill ASAP

TLDR: Context engineering is how you make AI agents useful in real-world tasks. It’s not about clever prompts. It’s about designing what the AI sees, knows, and can access before it replies. If agents are underperforming in your business, chances are they’re not dumb they’re working blind.

What Is Context Engineering?

Context engineering is the discipline of feeding AI models the right information, in the right format, at the right time.

If that sounds vague, it’s because we’ve been trained to think AI = prompt.

But prompts are just one input. Context engineering goes deeper. It shapes the entire environment the AI works within—before it even starts thinking.

Here’s what counts as context:

• Instructions (e.g. “act like a project manager” or system messages that define tone and task)
  
• User input (e.g. the actual question or request)
  
• Short-term memory (e.g. the previous five messages in the chat)
  
• Long-term memory (e.g. saved data about preferences, history, client files)
  
• Retrieved documents (e.g. PDFs, past reports, policy guidelines)
  
• Tools available (e.g. what the AI can do—send emails, access calendars, search knowledge bases)
  
• Output schema (e.g. “reply in a table format” or “respond with JSON”)
  

When people complain that “AI gave me something generic,” this is usually why: the model didn’t get the full picture.

Why This Matters Now

The timing isn’t random.

AI agents are getting built into everything: copilots, assistants, workflows. But as we move from one-off chats to autonomous agents running multi-step tasks, the model’s success isn’t about its IQ. It’s about what context it’s working from.

Put another way: you can have GPT-4 or Claude Sonnet 3.5 running your agent. Doesn’t matter. If it doesn't know enough about your customer, your pricing, your brand tone, or your past work, it’ll hallucinate or underdeliver.

That’s the gap context engineering solves.

Where Most People Go Wrong

Prompt libraries aren’t useless. But they won’t get you real traction unless you back them with contextual data.

Here’s how this plays out in the wild (the digital one):

• A small business owner uses a “marketing prompt” copied from Twitter. The AI suggests a funnel strategy that assumes $100K/month ad spend. They’ve got $800.
  
• A consultant builds a GPT agent for RFP responses. It misses half the requirements. Why? It had no memory of client credentials, bid history, or compliance rules.
  
• A manager asks for a product launch checklist. The model spits out generic SaaS advice. But they’re running a community garden project.
  

In every case, the AI wasn’t wrong. It was just working without context.

From Prompting to Systems Thinking

Karpathy (a big name in AI) called it “more art than tech.” He’s right. Good context engineers don’t write magic prompts. They build systems that frame the task properly: like setting the table before a guest arrives.

Here’s what that might look like:

1. Before the prompt, load business goals, customer types, pricing tiers, timelines, and tone of voice.
   
2. During the prompt, make sure the model accesses relevant documents, FAQs, and past work examples.
   
3. After the prompt, clean up the output using schema so the results slot cleanly into your actual workflows.
   
4. This isn’t prompt hacking. It’s systems design. And it’s the missing layer that turns “interesting outputs” into tools you trust and use every day.

Example: Marketing Advice That Actually Understands You (Like Really)

Let’s say you’re a consultant trying to launch a workshop. You ask AI for a marketing strategy.

Without context, you’ll get this:

“Run ads. Post on LinkedIn. Write a blog.”

Now add these:

• Audience: HR teams in local councils in Melbourne
  
• Budget: $3K
  
• Offer: 2-hour in-person session
  
• Constraints: You’ve got no design team
  
• Assets: Testimonials, deck, pricing sheet
  

Suddenly the output changes:

• LinkedIn carousel using testimonial quotes (designed with Canva)
  
• Outreach email template written for HR pain points
  
• Suggested lead magnet that ties into workshop content
  
• Same AI. Better input. Useful result.

Context Is the Operating System: YOU ARE the Context

If you’re serious about using AI to save time or scale output, stop collecting prompts like trading cards.  Start building systems that give AI what it needs to reason, decide, and act in your context. Because the model is not the bottleneck. Your inputs are.

FAQs

Q: Is this just prompt engineering with a fancy name?
A: No. Prompt engineering is about wording. Context engineering is about system design—what memory, tools, and documents the AI sees before you prompt.

Q: Do I need a developer to build this?
A: Not always. Tools like GPTs, Claude Artifacts, and Make.com let you set context without code. But complexity does grow fast with agent use.

Q: What’s the business case for this?
A: Context engineering avoids generic outputs, reduces hallucination, and increases the success rate of AI in workflows like marketing, document generation, and customer support.

Q: How do I start?
A: List what variables define your work: audience, product, tone, constraints, assets. Feed them into your model before asking it to act.

Q: Want to make AI useful instead of just impressive?
A: That’s the work we do: building low-code systems with the right context baked in. Agents that don’t just respond—they actually understand.