Most people don’t get much out of AI because they give it one sentence and expect magic. You wouldn’t give a junior dev a five-word Slack message and expect perfect results. So why do it with Claude or GPT?
Enter context engineering, the emerging art of setting AI agents up for real success. It’s more than prompting. It’s about shaping the entire environment around the task so your agent can reason, write, build, and improve with autonomy.
In this piece, we’ll show you why context engineering is the future of developer productivity, what it looks like in practice, and how you can start today.
Prompting vs Context Engineering
Let’s start by making the distinction clear. Here's what you're probably doing now, versus what you'll need to do next:
Prompting | Context Engineering |
|---|---|
"Write a login page" |
|
One-shot, ad hoc | Structured and reusable |
Treats AI like a freelancer | Treats AI like a team member |
Works only when supervised | Works asynchronously, in agent workflows |
Often unclear on outcomes | Defines outputs, constraints, and tests |
Most developers are using AI like a novelty. The professionals of tomorrow will use it like a team.
What Does Context Engineering Look Like?
Imagine you’re building a new feature: a customer-facing dashboard to visualise energy usage. With prompting, you might say:
“Write a React component to show kWh usage for the month.”
With context engineering, you’d prep a mini environment for the agent, containing:
requirements.md: Detailed feature overview, business context, user goalsdata_schema.md: Sample payloads from the backend APIdesign_guidelines.md: Styling conventions and branding rulesacceptance_tests.md: What "done" looks like, in plain languageoutput_format.md: File naming and structure expectationsprior_art.md: Similar components in the codebase for reference
Instead of hand-holding the model, you’ve handed it a spec pack. Just like you would with a new team member.
Real-World Example: Claude + Markdown = Velocity
In one recent experiment, we asked Claude 3 to build a file uploader component, but only after giving it a folder with five markdown documents. It navigated requirements, integrated with the provided API contract, matched the design system, and even wrote integration tests.
The most remarkable thing? We didn’t have to chat with it. The setup did the work.
📌 Takeaway: The chat window is not the only interface. The repo is now part of the prompt.
Start Context Engineering Today (Checklist)
Write it like you’re explaining the task to a freelancer who has time to read and no time to ask questions.
✅ Try this now: Take your next Jira ticket and turn it into a feature_name.md file with:
Summary
User story
Inputs and outputs
Edge cases
Constraints (tech, UI, performance)
2. Break Out Context Into Standalone Files
Create a folder of markdown files that tell the story of the task from multiple angles.
Minimum setup:
requirements.mdschema.mdoutput_contract.mdexamples.md
✅ Try this now: Build a template folder in your codebase you can duplicate for each new feature.
3. Use File References in Your Prompts
When using Claude or GPT-4o, refer to specific files using filenames or links.
Example:
“Use the details in requirements.md and match the API defined in schema.md to create the dashboard component. Output a single Dashboard.tsx file.”
4. Give the Agent Room to Think
Avoid micromanaging. Instead, create an environment rich enough for autonomous reasoning.
✅ Try this now: Ask the model to produce a plan or outline before it starts coding. If your context is strong, the plan will surprise you with its quality.
5. Design for Reusability
Don’t hardcode anything in your markdown. Use templates, variable brackets, and general principles where possible.
Where This Is Going
Soon, we’ll see repo-aware agents running asynchronously: pulling from context files, submitting PRs, running tests, and self-correcting.
Context engineers will be the architects of this future.
They’ll write markdown, define system boundaries, shape expectations, and orchestrate entire features through structured text, not code. It’s a high-leverage skill that merges product, dev, and AI fluency.
Think Like a Lead, Write Like a Wiki
Every time you brief a task to an AI, ask yourself:
“If I gave this exact folder to a competent human dev, would they know what to do?”
If not, you’re not doing context engineering yet.
