Jul 17, 2025·8 min
Vibe Coding: How Engineers Become Curators and Editors
Vibe coding shifts engineers from writing every line to guiding, reviewing, and shaping AI output. Learn workflows, skills, and safeguards.
Vibe coding shifts engineers from writing every line to guiding, reviewing, and shaping AI output. Learn workflows, skills, and safeguards.
“Vibe coding” is shorthand for a specific workflow: you describe what you want in natural language, an AI assistant drafts code, and you steer the result until it matches your intent. The AI does fast first-pass implementation; you do direction, selection, and verification.
The key idea isn’t magical productivity—it’s a shift in where your time goes. Instead of spending most of your effort typing boilerplate, wiring endpoints, or translating well-known patterns from memory, you spend more effort shaping the solution: clarifying requirements, choosing tradeoffs, and ensuring the final code is correct for your product.
In vibe coding, the engineer acts more like:
This role shift is subtle but important. AI can draft quickly, but it can also guess wrong, misunderstand constraints, or produce code that “looks right” while failing in production. The speedup is in drafting, not in responsibility.
Vibe coding works best when you treat AI output as a starting point, not an answer key. You still own:
This workflow is especially useful for product teams, startups, and solo builders who need to iterate quickly—shipping small slices, learning from feedback, and refining continuously—without pretending that code generation eliminates engineering judgment.
The biggest shift in vibe coding isn’t that engineers “stop coding.” It’s that the center of gravity moves from typing lines to shaping outcomes.
Traditionally, an engineer produced most of the first draft. You’d design the approach, implement it line by line, run it, fix what breaks, then refactor until it’s readable and maintainable. The keyboard was the bottleneck—and the most visible signal of progress was simply “more code exists now than before.”
With AI-assisted programming, the first draft becomes cheap. Your job shifts toward:
This shift is accelerating because the tooling is finally accessible: better models, faster feedback loops, and interfaces that make iteration feel conversational rather than a compile-run grind.
Even if an AI writes 80% of the characters, the engineer still owns the outcome. You’re accountable for correctness, security, performance, and safety—especially the “boring” stuff tools often miss: error handling, boundary conditions, data validation, and clear interfaces.
Vibe coding rewards engineers who can make strong calls: “Is this the right solution for our system?” and “Would I trust this in production?” That judgment—not raw typing speed—becomes the differentiator.
AI-assisted programming shines when the “shape” of the code is known and the main goal is speed. It’s weaker when the real work is figuring out what the software should do in messy, real-world situations.
When you can describe the task cleanly, AI can produce solid first drafts—often faster than starting from a blank file.
In these areas, vibe coding can feel “magical” because the work is largely assembling familiar patterns.
AI tends to stumble when requirements are implicit, domain-specific, or full of exceptions.
A model can sound confident while silently making up constraints, misreading data shapes, or choosing a library that conflicts with your stack.
AI reduces typing time (getting code onto the screen). But it can increase editor time—reviewing, clarifying requirements, running tests, debugging, and tightening behavior.
The productivity win is real when teams accept the trade: less keystroking, more judgment. The engineer’s job shifts from “write it” to “prove it works, is safe, and matches what we actually need.”
Treat your prompt like a lightweight spec. If you want production-ready code, don’t ask for “a quick implementation.” Ask for a change with a clear purpose, boundaries, and a way to verify success.
Begin with what the feature must do, what it must not do, and how you’ll decide it’s done. Include constraints like performance limits, supported environments, and “don’t break” requirements (backward compatibility, existing routes, schema stability).
A useful pattern is:
Large prompts invite large mistakes. Instead, loop in smaller steps:
This keeps you in control and makes review straightforward.
AI writes better code when it can “see” your world. Share existing APIs, coding style rules, and the file structure you expect. When possible, include examples:
Close each iteration by asking for a self-audit:
The prompt becomes the contract—and your review becomes verifying the contract is met.
AI-generated code is best treated as a proposal: a fast first draft that needs an editor. Your job shifts from “write every line” to “decide what belongs,” “prove it works,” and “shape it to match the codebase.” Fast teams don’t accept output wholesale—they curate it.
Read AI output the way you’d review a teammate’s PR. Ask: does this fit our architecture, naming conventions, and error-handling style? If something feels unclear, assume it’s wrong until verified.
Use diffs and small commits to keep changes understandable. Instead of pasting a 300-line rewrite, land a series of focused commits: rename + restructure, then behavior change, then edge cases. This makes regressions easier to spot and roll back.
When you see risky areas, add inline comments and questions for the AI to address. Examples: “What happens if this API returns null?” “Is this retry loop bounded?” “Can we avoid allocating inside the hot path?” This keeps iteration anchored to the code, not a vague chat transcript.
A short checklist prevents “looks good” reviews:
If you’re spending multiple prompt rounds patching a tangled function, stop and rewrite that section manually. A clean rewrite is often faster—and produces code you can confidently maintain next month.
AI can get you to “it runs” quickly. The professional shift is insisting on “it’s verified.” Treat generated code as a draft until it passes the same bar you’d expect from a teammate.
A good vibe-coding workflow produces artifacts you can trust: tests, clear error handling, and a repeatable checklist. If you can’t explain how you know it’s correct, it’s not done—it’s just lucky.
When requirements are clear (inputs, outputs, constraints), write tests first. This gives the AI a target and reduces wandering implementations.
When requirements are still fuzzy, generate the code, then write tests right after while context is fresh. The key is timing: don’t let “temporary” untested code become permanent.
AI tends to handle the happy path well and miss weird corners. Two practical patterns help:
Put assertions and validation where your system meets the outside world: API requests, file parsing, and especially database writes. If bad data gets in once, it becomes expensive forever.
A simple “done” checklist keeps quality consistent:
This is how speed stays sustainable.
Vibe coding can feel fast because it produces plausible code quickly. The main risk is that “plausible” is not the same as “correct,” “safe,” or “allowed.” Treat AI output as an untrusted draft that must earn its way into your codebase.
AI often fails in quiet ways: off-by-one logic, missing edge cases, incorrect error handling, or concurrency issues that only show up under load. It may also make incorrect assumptions about your architecture—like expecting a service to be synchronous, assuming a table exists, or inventing a helper function that looks consistent with your style.
A common failure mode is hallucinated APIs: the code compiles in the model’s imagination, not in your repo. Watch for “almost right” method names, outdated library usage, and patterns that were common two years ago but are discouraged now.
AI-generated code can introduce insecure defaults (weak crypto choices, missing authorization checks, unsafe deserialization, overly permissive CORS). Don’t accept security-sensitive changes without focused review and, where possible, automated scanning.
Privacy is simpler: don’t paste secrets, tokens, customer data, or proprietary code into tools unless your organization explicitly allows it. If you need help, sanitize inputs or use approved internal tooling.
Know your org’s policy on code provenance and licenses—especially for generated snippets that resemble public examples. When the change is high-impact (auth flows, payments, infra, data migrations), set an escalation rule: require a second reviewer, run the full test suite, and consider a lightweight threat model before merging.
Vibe coding works best as a team process, not an individual trick. The goal is to make AI output predictable, reviewable, and easy to improve—so your codebase doesn’t turn into a pile of “mystery code.”
Use the same workflow for most tasks:
task brief → AI draft → human edit → tests
The task brief is the key. It should define inputs/outputs, constraints, and acceptance criteria in plain language (and link to relevant files). Then the AI produces a first pass. A human makes the code production-ready: naming, structure, edge cases, error handling, and fit with existing patterns. Finally, tests and checks confirm it behaves correctly.
Break work into small, reviewable slices. Smaller PRs make it easier to spot wrong assumptions, subtle regressions, and mismatched style. If the AI proposes a big refactor, split it: first add tests, then change behavior, then cleanup.
To reduce “confident nonsense,” ask for explanations alongside the draft:
This gives reviewers something concrete to evaluate (performance, complexity, maintainability) before debating implementation details.
Track AI-influenced changes in PR descriptions. Not as a badge—just as context: what was generated, what was edited, and what you verified. This improves review quality and builds shared intuition about when AI suggestions are reliable.
Create reusable prompt templates for recurring tasks (new endpoint, data migration, CLI command, test suite additions). Templates turn one person’s prompting habits into a team asset—and make results more consistent across reviewers and repos.
AI can produce a lot of code quickly. The differentiator isn’t how fast you type—it’s how well you steer, evaluate, and integrate what gets generated.
Vibe coding rewards engineers who model the whole system: data flow, boundaries, and failure modes. When you can describe how requests move through services, where state lives, what happens on timeouts, and what “bad input” looks like, you can guide AI toward code that fits reality—not just the happy path.
Strong reading skills become a superpower. AI outputs can look plausible while subtly missing intent: wrong edge cases, misused libraries, leaky abstractions, or mismatched types. The job is to spot gaps between the requirement and what the code actually does—quickly, calmly, and without assuming correctness.
When generated code fails, you still need to localize the problem. That means logs that answer questions, metrics that show trends, and traces that reveal bottlenecks. AI can suggest fixes, but you need the discipline to reproduce issues, inspect state, and verify outcomes.
Clear requirements, crisp prompts, and good PR narratives reduce rework. Document assumptions, list acceptance criteria, and explain “why” in reviews. This makes AI output easier to validate and teammates faster to align.
Consistency, simplicity, and maintainability don’t appear by accident. Curators enforce conventions, remove unnecessary complexity, and choose the most boring solution that will still survive change. That judgment—more than keystrokes—determines whether vibe coding speeds you up or adds long-term cost.
AI can draft code quickly, but it won’t guarantee consistency, safety, or maintainability. The fastest vibe-coding teams treat the model as a generator and their tooling as the guardrails that keep output aligned with production standards.
Start with the tools that enforce conventions without debate:
AI is happy to import packages or copy patterns that are outdated.
Use PR tooling to focus attention on risk:
Reduce variance by giving the model a path to follow:
Where you run vibe coding affects what you can safely standardize. For example, platforms like Koder.ai wrap the chat-driven workflow with practical engineering controls: planning mode (so you can review a change plan before code is generated), source code export (so you’re never locked in), and snapshots/rollback (so experiments are easy to revert). If your team is generating React frontends, Go services with PostgreSQL, or Flutter mobile apps, having the stack conventions baked into the workflow can reduce variance across AI drafts.
The goal isn’t more tools—it’s a reliable pipeline where AI output is immediately formatted, checked, scanned, and reviewed like any other change.
Rolling out vibe coding works best as an experiment you can observe—not a big-bang mandate. Treat it like introducing a new build system or framework: pick a bounded area, define expectations, and measure whether it improves outcomes.
Start where mistakes are cheap and feedback is fast. Good candidates are internal tooling, a small service with clear inputs/outputs, or a self-contained UI component.
A useful rule: if you can revert the change quickly and validate behavior with automated checks, it’s a strong pilot.
Teams move faster when “what’s allowed” is explicit. Keep the first version short and practical:
If you already have engineering standards, link them and add an addendum rather than rewriting everything (e.g., “AI-generated code must meet the same review and test bar”).
Pick a small set of metrics and track them during the pilot:
The goal is to learn where AI helps and where it increases hidden costs.
After each sprint (or even weekly), collect examples:
Turn these into reusable prompt templates, review checklists, and “don’t do this” warnings.
Document what you learned in a central place (e.g., /engineering/playbook). Include:
Once the pilot is consistently positive, expand to the next area—without lowering the quality bar.
If you’re using a hosted vibe-coding environment (such as Koder.ai), standardization is often easier because the workflow is already structured around repeatable steps (plan, generate, review, deploy), with deployment/hosting and custom domains available when you want to move from prototype to production.
Vibe coding doesn’t remove engineers from the loop—it changes what “being in the loop” means. The highest-leverage work shifts from typing every line to deciding what should be built, constraining how it’s built, and verifying that the result is safe, correct, and maintainable.
When AI can draft implementations quickly, your advantage is judgment: picking the right approach, spotting subtle edge cases, and knowing when not to accept a suggestion. You become the curator of intent and the editor of output—guiding the model with clear constraints, then shaping the draft into something production-ready.
Yes, you can ship faster. But speed only counts when quality stays steady. The guardrails are the work: tests, security checks, code review discipline, and a clear definition of done. Treat AI as a fast junior contributor: helpful, tireless, and occasionally wrong in confident ways.
Reliable vibe coders don’t “feel” their way to completion—they review systematically. Build muscle memory around a lightweight checklist: correctness (including weird inputs), readability, error handling, performance basics, logging/observability, dependency risk, and security/privacy expectations.
Create two reusable assets:
With those in place, the job becomes less about raw typing speed and more about direction, verification, and taste—the parts of engineering that compound over time.
“Vibe coding” is a workflow where you describe intent in natural language, an AI drafts an implementation, and you steer it through review, edits, and verification until it matches real requirements.
The speedup is mostly in first-pass drafting, not in responsibility—you're still accountable for what ships.
Your role shifts from primarily typing code to curating and editing drafts:
It helps most when the task has a known shape and clear requirements, such as:
It often fails when requirements are implicit or messy:
Treat output as plausible drafts, not truth.
Include three things up front:
This turns the prompt into a lightweight spec you can verify against.
Use a tight loop:
Smaller iterations reduce large, hard-to-review mistakes.
Review it like a teammate’s pull request:
Prefer small commits and diffs so regressions are easier to spot.
Don’t stop at “it runs.” Require evidence:
Common pitfalls include:
Use dependency/secret scanning in CI, and escalate review for auth, payments, infra, or data migrations.
Make it a repeatable team process:
Document a shared checklist so “AI-generated” doesn’t become “mystery code.”