This content originally appeared on DEV Community and was authored by Sarah Guthals, PhD
In The Mythical Man-Month, Fred Brooks famously wrote:
The magic of myth and legend has come true in our time. One types the correct incantation on a keyboard, and a display screen comes to life, showing things that never were nor could be.
In 1975, that was programming itself; type the right sequence of symbols and something new appeared.
Today, we’re living through a new version of that magic. With large language models, you don’t even need the exact incantation. A vague prompt – a vibe – can conjure up working code. It feels like we’ve entered what I like to call The Mythical Vibe-Month: a world where AI gives us the illusion of infinite acceleration.
But here’s the rub: magic without context is messy.
Vibe Coding’s Surface-Level Problem
“Vibe coding” works beautifully in demos, toy projects, and small scripts. But in production systems, it misses the most important ingredient: context.
And I don’t just mean the context of the code snippet. I mean the living context of a software project:
- The Slack conversations that explain why a shortcut was chosen.
- The GitHub issues where trade-offs were debated.
- The PR comments that capture edge cases and gotchas.
- The searches, docs, and past LLM queries engineers already ran.
- The experiments, bugs, fixes, and fast follows that shaped today’s code.
LLMs can’t see any of that. So they often generate code that’s plausible, but out of tune with the broader system. This adds hidden complexity, reintroduces old bugs, or sometimes over-engineers where a simpler fix would have worked.
This mirrors what I studied in my grad-school days, where I focused on designing learning experiences that enculturate novices. Teaching novices syntax alone doesn’t make them programmers. They need the culture of programming: exposure to how experts debug, comment, negotiate trade-offs, and work together.
Without that context, their “magic” fizzles.
It’s a bit like (and forgive the reference…we’ll leave the author out of it 
) when a certain boy wizard mispronounces Diagon Alley and ends up somewhere entirely unintended. The spell was close enough to feel right, but without precision and context, he landed in Knockturn Alley instead.
AI today is in that same position. It can chant the incantations, but without the lived context of the codebase and its history, it often lands us in the wrong alley.
Context Engineering: The Antidote to Vibes Alone
This is where context engineering comes in: the discipline of giving AI systems the right information, in the right form, at the right time.
Instead of hoping an LLM vibes its way into correctness, context engineering means:
- Capturing rationale, history, and constraints alongside code.
- Distilling unstructured knowledge (docs, PDFs, logs, contracts) into structured signals.
- Connecting artifacts across the software lifecycle so AI can see the bigger picture.
- Making the invisible visible so the AI doesn’t just guess, it reasons.
With context, AI shifts from being a clumsy novice to a genuine collaborator.
At Tensorlake, this is exactly what we’re focused on but from the document perspective. It’s why I joined this company. This has been a problem since “before AI” because it’s a learning problem. We need to start addressing AI dev tools for vibe coding like we address AI data tools: unlock data that’s trapped in unstructured formats so that both humans and AI can use it as context. Not bigger models. Not longer prompts. Smarter inputs.
Why This Matters for Engineers
For engineers experimenting with AI, this is the difference between a parlor trick and a production tool:
- With just vibes: AI accelerates you today but introduces subtle complexity for tomorrow.
- With context: AI can understand systems, not just snippets.
But it’s not just about the AI. Engineers themselves should be using these AI-driven dev tools to learn.
Learning a new framework? Use AI to surface not just the docs, but the design decisions and trade-offs baked into them.
Trying to understand a legacy codebase? Use AI tools that highlight the history of changes, PR debates, and bugs fixed, not just the latest code snapshot.
Building awareness in a fast-moving team? Let AI summarize Slack threads, issues, and commits so you don’t miss evolving context.
In other words: don’t just let AI code for you. Let it teach you, by surfacing the cultural and contextual knowledge that makes the code what it is. That’s how engineers can stay enculturated in their own systems, even as those systems evolve.
Note: Please do all this responsibly. This is not the post to dive into ethics, but I hope you understand what “responsibly” means.
Closing Thoughts
Fred Brooks showed us that programming itself once felt like magic; typing the right incantation to summon something new. Today, AI has made that magic even more accessible. But without context, it’s the wrong kind of magic: flashy, fragile, and ultimately unsustainable.
When I think about my research on how people learn to program, the lesson for AI is the same: magic isn’t learned in isolation. It’s learned in community, through practice, feedback, and, most importantly, context .
If Brooks were writing today, I think he’d smile at the idea of The Mythical Vibe-Month. But he’d also remind us that engineering discipline is what makes software scale.
Vibes are the incantation.
Context is the curriculum.
And that’s what turns messy magic into real mastery.
This content originally appeared on DEV Community and was authored by Sarah Guthals, PhD