From Prompt Engineering to Context Engineering

Stephen Dixon

Stephen Dixon

— 3 min read
From Prompt Engineering to Context Engineering

There was a moment — not long ago — when prompt engineering felt like the future.

Threads went viral. Templates were shared. People built entire workflows around carefully crafted paragraphs sent to GPT.

And to be fair — it worked.

For demos. For experiments. For one-off interactions.

But if you’ve tried building a real product around large language models, you’ve likely felt the friction.

Because prompt engineering doesn’t scale.

What scales is context.

And that’s the shift we’re entering now.

The Prompt Era

Prompt engineering optimized for one thing:

What do I ask the model right now?

It rewarded clever phrasing, token tricks, role assignment, and structured instructions embedded inside long strings of text.

You could get surprisingly far.

But behind the scenes, something was brittle.

Every change in wording could alter output quality. Every new feature meant another layer of prompt complexity. Debugging became guesswork.

In small tools, that’s manageable.

In products? It becomes chaos.

Where Prompt Engineering Breaks

If you’ve shipped anything AI-powered, you’ve probably experienced at least one of these:

  • A prompt that worked perfectly… until you added one more variable.
  • A model response that changed dramatically after a minor refactor.
  • Difficulty reproducing outputs because context was implicit.
  • Endless tweaking instead of architectural clarity.

The core issue isn’t the model.

It’s that prompt engineering treats intelligence like a one-off interaction.

There’s no structure. No separation between data and instruction. No durable boundary between state and intent.

You end up with what I call prompt soup — everything jammed into a single string, hoping the model “gets it.”

That’s not architecture.

That’s improvisation.

The Shift: Context Engineering

Prompt engineering optimizes what you ask.

Context engineering optimizes what the model already knows.

That distinction changes everything.

Instead of focusing on crafting the perfect sentence, you focus on designing structured inputs:

  • Who is the user?
  • What are their goals?
  • What just happened?
  • What constraints apply?
  • What system state matters?

These aren’t prose paragraphs.

They’re typed, composable context blocks.

Example shape (paste this into Ghost as JSON and wrap it in a code block on your side):

{
  "user_profile": { ... },
  "task_context": { ... },
  "time_context": { ... },
  "system_state": { ... }
}

The prompt becomes thinner.

The context becomes richer.

And suddenly, your AI layer starts behaving less like a magic box — and more like a system.

This is the philosophical shift behind Model Context Protocol (MCP). It formalizes what many builders have discovered the hard way: structured context scales, clever strings don’t.

Why This Changes Product Design

This isn’t just an engineering tweak.

It’s a product-level shift.

When context becomes first-class, you stop asking:

“How do I get the model to say the right thing?”

And start asking:

“What should the model understand before it responds?”

That leads to better UX decisions:

  • What should persist across sessions?
  • When should context expire?
  • What signals are relevant in this moment?
  • How do we avoid overwhelming the model with noise?

This is where AI-native UX begins.

The best AI products won’t feel clever.

They’ll feel aware.

They won’t overwhelm users with output.

They’ll respond with relevance.

And relevance is a function of context, not prompt phrasing.

The Architecture of the Next Wave

Here’s my prediction:

In a year or two, nobody will brag about their prompts.

They’ll talk about their context layers.

We’ll see:

  • Context engines that assemble structured input dynamically
  • Reusable schemas for common flows
  • Visual tools for debugging what the model “knew”
  • Clear separation between memory, instruction, and action

Prompt files will look primitive.

Context pipelines will look normal.

And the apps that win won’t be the ones with the flashiest demos.

They’ll be the ones where the AI feels grounded, consistent, and calm.

This Is a Design Discipline

Context engineering sits somewhere between:

  • backend architecture
  • UX design
  • systems thinking

It requires restraint.

You don’t dump everything into the model.

You curate what matters.

You define boundaries.

It’s not about making the model smarter.

It’s about making your system clearer.

That’s the difference between hacking something together — and building something that lasts.

Where I’m Taking This

This shift is shaping how I build.

Inside apps like AteIQ, the focus isn’t on clever prompts. It’s on structured inputs, evolving context, and clear state modeling. The prompt is just the final step.

In upcoming posts, I’ll explore:

  • What AI-native UX really looks like
  • How I structure AI features in SwiftUI
  • Why calm, context-aware products outperform noisy ones

If you’re exploring this shift too, I’d genuinely love to connect or you can drop me a line.

And if you're just getting started, I hope this blog becomes a place you revisit and grow alongside.

Until next time!

Read more