Your job is now Context Engineer

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:

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:

With context engineering, you’d prep a mini environment for the agent, containing:

• requirements.md: Detailed feature overview, business context, user goals

• data_schema.md: Sample payloads from the backend API

• design_guidelines.md: Styling conventions and branding rules

• acceptance_tests.md: What "done" looks like, in plain language

• output_format.md: File naming and structure expectations

• prior_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.md

• schema.md

• output_contract.md

• examples.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:

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 not, you’re not doing context engineering yet.