"Prompt engineering" got boring, so the industry needed a new term. "Context engineering" showed up right on schedule — same energy, fresher packaging. LinkedIn posts about it. Conference talks. A few people selling courses. The usual cycle.
But here's the thing. Dismiss the term all you want — I certainly do — and you still have the problem it describes. When you work with language models daily, the single biggest determinant of output quality isn't which model you use. It's what information you give it and how you structure that information. That's real. That matters. You don't need a buzzword to do it, but you do need to actually think about it.
What people actually mean
Strip away the hype and "context engineering" refers to something practitioners have been doing since the first person got frustrated with a chatbot response and thought "maybe I should explain what I actually want."
It's this: the model doesn't know what you know. It doesn't know your project structure, your conventions, your constraints, what you tried yesterday that didn't work. Every interaction starts from zero unless you deliberately provide that information. The quality of what you provide — its relevance, its structure, its density — determines the quality of what you get back.
That's it. That's the whole insight. Everything else is implementation detail.
The context hierarchy — the one diagram worth keeping
Not all context carries equal weight. There's a natural hierarchy, and understanding it is genuinely useful:
┌─────────────────────────┐ │ System Instructions │ ← Who the model is, how it behaves ├─────────────────────────┤ │ Task Definition │ ← What needs to happen right now ├─────────────────────────┤ │ Examples & Patterns │ ← What good output looks like ├─────────────────────────┤ │ Current Context │ ← The immediate data, code, or text ├─────────────────────────┤ │ Supplementary Info │ ← Background that might be relevant └─────────────────────────┘
Higher in the stack means more influence on model behavior. System instructions shape everything downstream. Supplementary info at the bottom gets the least attention — and if your context window is full, it's the first thing the model effectively ignores.
Most people get this backwards. They dump everything into the supplementary layer — paste in a whole codebase, a full document, a sprawling requirements spec — and then wonder why the model's response is vague. The model isn't stupid. You just buried the signal in noise.
CLAUDE.md is context engineering
I use Claude Code every day. The single most impactful thing in my workflow isn't a fancy prompt template or a Python class called ContextCompressor. It's a file called CLAUDE.md that sits in my project root.
It tells the model what this project is. What the directory structure looks like. What commands to run. What conventions to follow. It's a few dozen lines of markdown — nothing clever, nothing proprietary. But it means that every time Claude opens a session in this repository, it already knows the architecture, the tech stack, and how things fit together. I don't have to re-explain it. The context is just there.
That's context engineering. Not a framework. Not a library. A markdown file that answers the questions the model would otherwise have to guess at.
And it works because it hits the top of the hierarchy — system-level instructions that shape everything the model does in that session. If I put the same information in a casual message halfway through a conversation, it would carry less weight. Position matters.
Too much context is as bad as too little
There's a mistake I see constantly, and I've made it myself: the instinct to give the model everything. Paste in the entire file. Include all the docs. Provide the full conversation history. More information means better results, right?
Wrong. Language models have attention mechanisms, not magic. When you flood the context window, relevant information competes with irrelevant information for the model's attention. The thing you actually need the model to focus on — the specific function, the particular requirement, the exact error — gets diluted by everything else you threw in.
I learned this the hard way building with Claude Code. Early on, I'd point it at entire directories. "Read all of src/components/ and then fix this bug." The results were mediocre. When I started being specific — "read src/components/featured-posts.tsx, the bug is in the sorting logic around line 40" — the results got dramatically better. Same model. Same problem. Different context structure.
The skill isn't maximizing information. It's maximizing the ratio of relevant information to total information. Sometimes that means giving the model less.
Structure matters more than volume
Here's something that isn't obvious until you've done this enough: how you organize context changes what the model does with it. The same information presented as a wall of text versus structured with clear headers, explicit labels, and logical grouping produces meaningfully different outputs.
This is why CLAUDE.md files use markdown with headers and tables. This is why good system prompts use clear sections. This is why tool descriptions in agent frameworks matter — they're context that tells the model when and how to use each capability.
When I write tool descriptions for my agent projects, I've noticed that a vague description like "searches the codebase" produces worse tool selection than "searches file contents using regex patterns — use for finding function definitions, variable usage, or specific strings across the project." Same tool. Different context about the tool. The model makes better decisions with the structured version because the context gives it clearer signal about when the tool is appropriate.
The buzzword lifecycle
"Prompt engineering" was the first buzzword. It implied that getting good results from language models was a specialized skill — almost a new profession. That was half true. There are real skills involved. But the framing was wrong. It wasn't engineering. It was communication — figuring out how to be clear and specific with a system that takes you literally.
"Context engineering" is the second buzzword. It sounds more technical, more serious. It implies systems thinking, architecture, maybe even a new discipline. Some people are building real tools around it — context management systems, retrieval-augmented generation pipelines, agent memory architectures. That work is legitimate. But the term itself is still marketing.
I expect a third buzzword within a year. Maybe "cognitive architecture" or "agent orchestration" or something with "agentic" in it. The cycle will repeat. And underneath each buzzword, the same actual skill will persist: figure out what the model needs to know, give it that information in a clear structure, and don't bury the important stuff.
What actually works
After a year of building with language models daily, here's what I've found matters:
Be specific about the task before providing context. Tell the model what you want it to do, then give it what it needs to do it. Not the other way around. "Fix the type error in this component" followed by the component is better than the component followed by "find any issues."
Use the model's own structure against it. Models are trained on markdown, code comments, docstrings, READMEs. They parse structured text better than prose. Use headers, use bullet points, use code blocks. Not because it's pretty — because the model's attention mechanisms handle structured text more effectively.
Know when to give the whole file vs. the relevant snippet. This is judgment, not algorithm. If the bug might be caused by something elsewhere in the file, give the whole file. If you know exactly where the problem is, give the relevant section plus enough surrounding context to make it unambiguous. There's no formula. You develop a sense for it.
Write good CLAUDE.md files. If you use Claude Code or any tool that reads project configuration, the five minutes you spend writing a clear project description will save you hours of re-explaining context. This is the highest-leverage context engineering you can do, and it requires zero technical sophistication.
Treat context like a limited resource. Because it is. Even with models that have 200k token context windows, attention degrades with volume. The question isn't "can I fit this in the window?" It's "does including this help or hurt?"
The real skill
Context engineering — or whatever we end up calling it — isn't a discipline. It's a literacy. The same way that learning to write clearly is a literacy, or learning to read code is a literacy, learning to structure information for language models is becoming a baseline skill for anyone who works with AI.
You don't need a ContextCompressor class. You don't need a DynamicContextManager. You don't need a course. You need to pay attention to what works, notice when your prompts fail, and ask yourself: did the model have what it needed? Was the important information where the model could find it? Did I bury the signal in noise?
The term is hype. The problem is real. And the gap between people who think about this and people who don't is widening every month — not because the skill is hard, but because most people never stop to think about it at all.