A few months back, “vibe coding” was all the rage. You’d fire up your AI assistant, toss in a half-baked idea, and watch code appear. It felt incredible, right up until you tried to ship that code to production and realized your fancy new app was held together by duct tape and vibes.

Turns out vibes don’t scale. Context does.

That’s where context engineering comes in. It’s the idea that if you want your AI tools to actually do what you want, you have to feed them the right information, in the right way, from the start.

From Vibes to Context #

Andrej Karpathy, who gave us the “vibe coding” term, summed up context engineering pretty well in a tweet:

“The art of providing all the context for the task to be plausibly solvable by the LLM.”

Translation: your AI is not a mind reader. It needs guardrails, examples, and instructions to produce solid results. Otherwise, it hallucinates, makes things up, or grabs the first random pattern it can find.

That’s why, according to Codto’s State of AI Code Quality, 76.4% of developers are hesitant to ship AI-generated code without human review. The code looks right, until it’s not.

Context Engineering vs. Prompt Engineering #

Some folks think context engineering is just prompt engineering in a tuxedo. It’s not. Prompt engineering is still useful, and writing thoughtful, well-structured inputs definitely helps, but it’s just one part of the equation. Context engineering zooms out and asks: what does the model need to know to reliably do the job? Not just the question, but the rules, examples, past attempts, architectural preferences, tooling, and structured formats that all build a more complete picture. You’re not just writing a clever prompt; you’re designing a working environment for your AI collaborator. That’s the shift.

Haven’t We Been Doing This Already?

Kind of. We’ve already been doing bits and pieces of this, whether we realized it or not. If you’ve ever used RAG to pull external docs into a prompt, or if you’ve broken a big job into subtasks with individual agents handling their part, or even just wired up an AI assistant with API access so it could grab relevant data on the fly, then congratulations. You’ve done some form of context engineering.

The difference now is we’re naming it and treating it like a proper discipline. Instead of duct-taping facts together at runtime, we’re taking a step back and designing context with the same care we give to architecture or deployment. Less chaos, more intention. And ideally, fewer facepalms later.

The Anatomy of Context Engineering #

Context engineering looks simple on paper but packs a punch in practice. You set clear instructions and rules so your AI follows the right conventions, then give it memory of what happened earlier in the session or even across past sessions. You enrich it with external knowledge via RAG, link it to the right tools so it doesn’t make things up, and insist on structured outputs like JSON or Pydantic models to keep results predictable. When you treat these elements as part of a single, engineered system instead of random add-ons, you avoid hallucinations, confusion, and code that falls apart at scale.

In the real world #

Here’s a day in the life of context engineering: you draft a crisp feature spec in Markdown, then stash a handful of code snippets, API docs, and style guides in a context folder. A single command turns all of that into a mini spec sheet containing goals, success criteria, dependencies, file layout. Then your AI assistant takes over. It scaffolds the project, writes tests, refines functions, and updates the readme while you grab a coffee. You come back to a healthy codebase that reflects your conventions and edge cases, not wild hallucinations. That is context engineering in action.

In summary… #

Vibe coding shines for quick prototypes, but it’s context engineering that keeps production systems afloat when stakes are high.

Failures in AI systems usually stem from messy context rather than flawed models, and context engineering turns assistants into reliable collaborators.

Treating context engineering as a core practice turns rough prototypes into production-ready software and keeps your AI assistant building reliable code rather than chasing vibes. If you’ve got six hours to chop down a tree, spend four sharpening your axe.