So, Are You Really Vibe Coding? The 2026 Definition, Lexicon, and Decision Framework

AI building became a spectrum in 2026. Here's the updated definition, the A–Z lexicon many builders keep getting wrong, and how to choose the right build mode before it touches real users.

Karo (Product with Attitude)

Jun 26, 2026

∙ Paid

TL;DR: Vibe coding is using natural language prompts to model, prototype, and build products with AI: you describe what you want in conversation and iterate on what the AI generates instead of writing every line yourself. In 2026 it is no longer one workflow. It is a spectrum, from fast conversational prototyping to spec-first AI-assisted development, and your job is knowing how much specification a project needs before it touches real users. The term was popularized by Andrej Karpathy on February 2, 2025, and on February 8, 2026, almost exactly one year later, he reportedly called the framing passé and moved past it toward agentic engineering. This guide gives you the updated 2026 definition, a full AI development lexicon (product thinking, design thinking, prompt engineering, context engineering, vibe coding, AI-assisted engineering, spec-driven development, agentic coding, loop engineering, agentic engineering, and critical AI literacy), and a 5-question decision framework for choosing how much structure your build needs.

How sure are you that what you’re doing is vibe coding?

I’m asking because many people use this term loosely and treat it as a synonym for any AI-assisted coding.

Some people, like me, use the term loosely on purpose.

Others are simply not yet familiar with the distinction.

Read this guide even if you’re already a pro builder.

Use it as a reference. Print it if you still believe in paper. Send it to your team if they keep calling everything “vibe coding.”

It gives you the vocabulary to describe what kind of AI builder you are.

The AI building spectrum across 10 stages grouped into four modes — Define, Steer, Build, Delegate — each showing how execution shifts from human to AI. 1. Product thinking (Define): you decide whether the thing should exist; human defines problem, user, value, trade-offs, success criteria; AI maps options, risks, and gaps. 2. Design thinking (Define): you shape the solution around real people; human researches, frames, prototypes, tests; AI synthesizes research and stress-tests assumptions. 3. Prompt engineering (Steer): you steer the model with better instructions; human writes clear tasks, constraints, examples; AI responds to instruction quality. 4. Context engineering (Steer): you give the model the right working environment; human curates docs, rules, product context, test cases; AI uses richer context to reason. 5. Vibe coding (Build): you build through conversation; human describes, tests, refines, judges the product; AI generates code from intent. 6. AI-assisted engineering (Build): AI is a coding collaborator; human owns architecture, review, testing, security, approval; AI drafts, refactors, and debugs. 7. Spec-driven development (Delegate): the spec is the source of truth; human defines requirements and acceptance criteria; AI implements against the plan. 8. Agentic coding (Delegate): you delegate multi-step work; human sets objective, scope, guardrails, definition of done; AI plans, edits files, runs checks, reports. 9. Loop engineering (Delegate): you design the system that prompts the agent; human builds the repeatable process, triggers, memory, escalation paths; AI executes inside the loop until done or blocked. 10. Agentic engineering (Delegate): you manage AI agents as part of the engineering system; human owns system design, governance, quality, safety, accountability; AI operates as part of a semi-autonomous build system. Across all 10 stages, AI execution rises and human execution narrows to ownership and judgment.

What’s Inside

Hey, I’m Karo Zieminski 🤗

AI Product Manager and builder.

I write Product with Attitude, an AI newsletter for thousands of subscribers developing critical AI literacy the only way it sticks: through practice.

We don’t just use AI. We build workflows, automations, and products with it, while studying how AI itself is built, positioned, and woven into our work.

If you’re new here, welcome! Here’s what you might have missed:

Part 1:
The 2026 Definition of Vibe Coding

Vibe coding is using natural language prompts to model, prototype, and build products with AI. You describe the product in conversation. The AI generates the code. You react to what it built, refine the prompt, and iterate until it works, or until you understand why it doesn’t.

Three things separate vibe coding from AI-assisted coding:

1. Natural language is the primary interface. You’re steering with intent, not syntax.

2. You evaluate outcomes, not code. You judge the running product, the behavior, the failure modes, without reading every line the AI wrote.

3. Iteration replaces specification. The plan takes shape through the conversation instead of being written upfront.

The term comes from Andrej Karpathy, who described the practice on February 2, 2025.

What started as a meme about “giving in to the vibes” became the default way thousands of non-traditional builders build real software.

By early 2026, Karpathy himself had moved on.

On February 8, 2026, he reportedly called the original framing passé and redirected attention toward agentic engineering, as covered by kingy.ai.

Vibe coding raises the floor, so non-technical people can build.
Agentic engineering raises the ceiling, so expert builders can ship far more.

That marks where the vibe coding term sits today: one mode on a wider spectrum. More on that below.

What Vibe Coding Is Not

The term gets messy because people keep merging four separate things.

Vibe coding is not a synonym for “any AI-assisted coding.”

This is probably the one making the term blur fastest.

Simon Willison called it semantic diffusion: a precise term spreads, people use it loosely, and the original meaning gets muddy.

By 2026, the split is obvious.

In casual conversation, “vibe coding” often means any kind of building with AI.

But in more serious technical writing, the meaning has narrowed. It describes the low-governance end of AI-assisted building: shipping without enough review, structure, or accountability.

A senior engineer who uses AI to draft a function, then reviews every diff, is doing AI-assisted engineering.

A beginner who chats an app into existence and discovers authentication later is vibe coding.

Same tools.

Very different accountability.

Vibe coding is not no-code.

No-code gives you pre-built blocks. Vibe coding produces real code you own, can read (or have AI explain), and can take anywhere.

You notice the difference when you outgrow the platform, or when the platform changes and your compliance risk changes with it.

Vibe coding is not slop coding.

Slop coding is vibe coding minus judgment, shipped anyway.

Speed without judgment produces AI slop: generic, emotionless, low-quality output generated without human taste, or intentional direction.

Slop coding is what happens when a poorly executed attempt at vibe coding turns your build into a magnificent disaster: you outsource your taste, compromise on product thinking, or skip intentional direction.

Vibe coding is not the enemy of good engineering.

Vibe coding and spec-driven are two ends of the AI-assisted software development spectrum. In reality, many serious builds live somewhere in the middle: fast enough to learn, structured enough to survive contact with users.

Some projects can start with a rough idea, and that’s fine: a prototype, a landing page, a small internal tool, a personal automation, or a feature you are still feeling your way through. Vibe coding gives you a fast iteration loop before the shape of the build is fully known.
Other projects need proper specs from the beginning: user stories, roles, data models, security constraints, compliance requirements, acceptance criteria, edge cases, deployment plans, and migration paths.