An Illustrated Guide to Context Engineering, Prompt Engineering, and The Future of Both
What I've learned about context engineering as an AI PM. Plus the Skill and the prompt I use to pressure-test it.
TL;DR: Context engineering is a systems design discipline that separates working AI from failing AI. It replaces prompt engineering as a core skill for product and workflow builders in 2026. The four canonical strategies are Write, Select, Compress, and Isolate (LangChain framework). Memory architecture splits into episodic, semantic, and procedural layers. Stanford’s ACE framework enables self-improving agents. For PMs and builders, this means owning context architecture as a product decision, not delegating it to engineering.
Last week, I came across a job post that asked for context engineering skills, but the responsibilities clearly described prompt engineering.
These are not synonyms.
Prompt engineering is deciding what and how to ask the model.
Context engineering is deciding what the model knows when it answers.
One starts a conversation. The other shapes the information ecosystem in which that conversation happens, so AI can complete tasks reliably.
Context engineering is not a better version of prompt engineering. It’s the system around the prompt.
Right now, as everyone offloads work to agents, our job is shifting. We’re learning to do two things extremely well: give the agent the right context, and judge the output. That's the new work.
This post is everything I’ve learned about context engineering as an AI PM and product builder. If someone had shared this level of detail with me when I was starting, I would have saved a lot of hours, credits, and headaches.
Hey, I’m Karo Zieminski 🤗
AI Product Manager and builder.
I write Product with Attitude, an AI newsletter community of 18K subscribers learning to build with AI and developing critical AI literacy through practice. The kind where you sit down on a Saturday morning, follow a guide, and walk away with a working agent, automation, or product. Built by you. Understood by you. Owned by you.
If you’re new here, welcome! Here’s what you might have missed:
→ The Only AI Prompting Guide That Works On Reasoning Models (And Our Cognition)
→ Claude Design Review: 48-Hour Builder’s Test + Hero Prompts
Join 18K readers from around the world and learn with us.
What’s inside
Why prompt engineering is no longer enough
Why bigger context windows do not solve bad context
The 4 strategies every reliable AI system needs
The 6 signs your context window is getting dirty right now
Why PMs should own context architecture as a product decision
How Stanford’s ACE framework points toward self-improving agents
A context window hygiene Skill you can copy
A context architecture prompt you can copy
Part 1
Context Engineering vs Prompt Engineering
The Tweets That Turned Context Engineering Into a Discipline
The term crystallized in June 2025, when Shopify CEO Tobi Lütke posted on X:
I really like the term ‘context engineering’ over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.
Andrej Karpathy agreed:
[…] the delicate art and science of filling the context window with just the right information for the next step.
That tweet went viral. The mental model they introduced is now canonical.
The LLM is the CPU: the brain.
The context window is RAM: the short-term memory.
We are the operating system: responsible for what gets loaded into that memory.
Our job is to load exactly the right data into working memory for each step. Too little context, and the AI guesses. Too much irrelevant context, and the AI gets confused.
The skill is knowing what to include, what to leave out, and when to update it.
Context Engineering vs. Prompt Engineering
As language models graduated from simple instruction followers to full-blown reasoning engines and agentic coworkers, the way we work with them had to evolve too.
Prompt engineering, the skill everyone obsessed over early on, is now just one part of context engineering. Understanding the distinction determines whether we build temporary demos or systems.
Context Engineering vs. Prompt Engineering:
A Comparison Table
Context Engineering vs. Prompt Engineering:
When to Use Which
Most confusion around prompt vs. context engineering comes down to when to use each.
Prompt engineering is fast, lightweight, and works well for one-off tasks.
Context engineering takes more setup (and maintenance!), but it’s what makes systems reliable over time.
This table shows which one to use based on the situation we’re in.
Weak Context Kills Strong Prompts
Gartner predicts 40% of enterprise applications will use task-specific AI agents by late 2026. Every one of those agents lives or dies by how well its context is engineered.
A well-crafted prompt in a poorly engineered context still fails. A poorly crafted prompt in a well-engineered context often succeeds.
That asymmetry is the argument for treating context as the underlying system.
