Nov 20, 2025·8 min
Vibe Coding: Turning Code Into a Conversation With AI
Learn how vibe coding shifts coding from rigid specs to dialogue—what changes in roles, workflows, and quality checks, plus practical ways to stay in control.
Learn how vibe coding shifts coding from rigid specs to dialogue—what changes in roles, workflows, and quality checks, plus practical ways to stay in control.
“Vibe coding” is a simple idea: instead of building software by writing every line yourself, you build it through an ongoing conversation with an AI that proposes code, explains trade-offs, and iterates with you.
You steer with intent (“make this page load faster,” “add sign-in,” “match this API shape”), and the AI responds with concrete changes you can run, inspect, and revise.
Traditional workflows often look like: write a detailed spec → break into tasks → implement → test → revise. That works well, but it assumes you can predict the right design up front and that writing code is the main bottleneck.
Vibe coding shifts the emphasis to: describe the goal → get a draft implementation → react to what you see → refine in small steps. The “spec” isn’t a big document—it’s an evolving dialogue paired with working output.
Three forces are pushing this shift:
Vibe coding shines when you’re exploring, prototyping, integrating common patterns, or polishing features through quick micro-iterations. It can mislead when you treat AI output as “correct by default,” especially around security, performance, and subtle business rules.
The useful mindset is: the AI is a fast collaborator, not an authority. You’re still responsible for clarity, constraints, and deciding what “done” means.
Traditional specs are designed to squeeze ambiguity out of a problem before anyone writes code. They try to freeze decisions early: exact fields, exact states, exact edge cases. That can be useful—but it also assumes you already know what you want.
Vibe coding flips the sequence. Instead of treating uncertainty as failure, you treat it as material for exploration. You start with intent and let the conversation surface the missing parts: constraints, trade-offs, and “oh, we didn’t think of that” moments.
A spec says: “Here is the system.” A conversation asks: “What should the system do when this happens?” That question-first approach makes it easier to discover requirements that were never going to show up in a document anyway—like how strict validation should be, what error messages should say, or what to do when an email is already taken.
When AI can draft an implementation in minutes, the goal of the first pass changes. You’re not trying to produce a definitive blueprint. You’re trying to produce something testable: a thin slice you can click, run, or simulate. The feedback from that prototype becomes the real requirements.
Progress is no longer “we finished the spec.” It’s “we ran it, saw the behavior, and adjusted.” The conversation produces code, the code produces evidence, and the evidence guides the next prompt.
Instead of writing a full PRD, you can ask:
That turns a vague desire into concrete steps—without pretending you already knew every detail. The result is less upfront paperwork and more learning-by-doing, with humans steering decisions at each iteration.
Vibe coding doesn’t replace “developer” so much as it makes the work feel like distinct hats you wear—sometimes in the same hour. Naming those roles helps teams stay intentional about who decides what, and prevents the AI from quietly becoming the decision-maker.
The Director defines what you’re building and what “good” means. That’s not just features—it’s boundaries and preferences:
When you act as Director, you don’t ask the AI for the answer. You ask for options that fit your constraints, then choose.
The Editor turns AI output into a coherent product. This is where human judgment matters most: consistency, edge cases, naming, clarity, and whether the code actually matches intent.
A useful mindset: treat AI suggestions like a draft from a fast junior teammate. You still need to check assumptions, ask “what did we forget?”, and ensure it fits the rest of the system.
The Implementer role is where the AI shines: generating boilerplate, wiring endpoints, writing tests, translating between languages, or producing multiple approaches quickly.
The AI’s best value is speed and breadth—proposing patterns, filling gaps, and doing repetitive work while you keep the steering wheel.
Even if the AI wrote 80% of the lines, humans own the outcomes: correctness, security, privacy, and user impact. Make that explicit in your workflow—who approves changes, who reviews, who ships.
To keep collaboration healthy:
The goal is a conversation where the AI produces possibilities—and you provide direction, standards, and final judgment.
Vibe coding shifts the default unit of work from “finish the feature” to “prove the next small step.” Instead of writing one giant prompt that tries to predict every edge case, you iterate in tight loops: ask, generate, test, adjust.
A useful rule is to move from big upfront requests to small, testable increments. Ask for a single function, a single endpoint, or one UI state—not the whole module. Then run it, read it, and decide what to change.
This keeps you close to reality: failing tests, real compile errors, and concrete UX issues are better guidance than guesswork.
Micro-iterations work best when you keep a steady rhythm:
Plan: define the next increment and success criteria.
Code: have the AI generate only what matches that plan.
Verify: run tests, lint, and do a quick read-through.
Refine: update the plan based on what you learned.
If you skip the plan step, the AI may produce plausible-looking code that drifts from your intent.
Before it writes code, ask the AI to restate requirements and assumptions in its own words. This surfaces gaps early: “Should we treat empty strings as missing?” “Is this synchronous or async?” “What’s the error format?” You can correct course in one message instead of discovering mismatches later.
Because decisions happen through dialogue, maintain a lightweight changelog: what you changed, why you changed it, and what you deferred. It can be a short section in your PR description or a simple notes file. The payoff is clarity—especially when you revisit the feature a week later or hand it to someone else.
If you’re using a vibe-coding platform such as Koder.ai, features like planning mode, snapshots, and rollback can make these micro-iterations safer: you can explore quickly, checkpoint working states, and undo experiments without losing momentum.
Vibe coding works best when prompts sound less like “write me a function” and more like “help me make a good product decision.” The hidden skill isn’t clever wording—it’s being explicit about what success means.
Begin by describing the situation the code will live in: goals, users, constraints, and non-goals. This prevents the model from filling gaps with assumptions you didn’t choose.
For example:
Before committing to an implementation, request multiple approaches with pros/cons. You’re not just generating code—you’re selecting trade-offs (speed vs. maintainability, accuracy vs. complexity, consistency vs. novelty).
A useful prompt pattern:
“Give me 3 approaches. For each: how it works, benefits, risks, what I’d need to verify. Then recommend one based on my constraints.”
AI can produce convincing happy-path output. Counter that by asking it to self-audit with a checklist: edge cases, error states, accessibility, and performance. This turns prompting into lightweight product QA.
Ask for minimal examples first, then expand. Start with a thin slice you can run and understand, then iterate: MVP → validation → polish. This keeps you in control and makes mistakes cheaper to spot early.
When an AI proposes code, it feels less like “writing” and more like “accepting or rejecting” options. That shift is exactly why quality control matters: suggested code can be plausible, fast, and subtly wrong.
Generated code should be handled like a first pass from a teammate who worked quickly and didn’t run anything. Assume it needs edits, verification, and alignment with your conventions before it earns a place in the codebase.
Run your usual review checklist, even if the change is small:
If the code is hard to read, it’s hard to trust—and harder to maintain.
Before you merge anything, ask for a plain-language explanation of what the code does, key assumptions, and edge cases it might miss. If the explanation is vague or avoids specifics, that’s a signal to slow down and simplify.
Ask the AI to propose tests that prove behavior, not just intent:
Even lightweight tests force clarity. If you can’t test it, you don’t really control it.
Only accept suggested code when you can (1) explain it, (2) run it, and (3) verify it with tests or reproducible checks. Speed is great—until it ships uncertainty.
Vibe coding shines when you’re exploring, prototyping, or iterating on well-understood patterns. It breaks when the AI starts “helping” by filling gaps you didn’t realize were there.
AI suggestions often contain unspoken guesses: which database you use, how auth works, what “active user” means, or what error handling is acceptable. Those assumptions can be subtle enough to look reasonable in a diff—but wrong for your product.
A practical tell: if the code introduces new concepts you didn’t mention (a cache, a queue, a specific library), treat it as a hypothesis, not an answer.
Models can invent APIs, flags, or entire methods that don’t exist—especially with fast-moving frameworks. The tone is persuasive, which can trick teams into shipping fiction.
Ways to catch it quickly:
An AI can optimize for test satisfaction while missing real needs: accessibility, latency, edge cases, or business rules. Passing tests may only prove you tested the wrong thing.
If you find yourself writing more and more tests to justify a questionable approach, step back and restate the user outcome in plain language before continuing.
Stop prompting and consult official docs (or a human expert) when:
Vibe coding is a fast conversation, but some decisions need a referenced answer—not a fluent guess.
Vibe coding moves a lot of thinking into the chat window. That’s useful—but it also makes it easier to paste things you wouldn’t normally publish.
A simple rule helps: treat every prompt like it could be logged, reviewed, or leaked. Even if your tool promises privacy, your habits should assume “shareable by accident.”
Some information is a hard “no” in prompts, screenshots, or copied logs:
If you’re unsure, assume it’s sensitive and remove it.
You can still get help without exposing real data. Replace sensitive values with consistent placeholders so the model can reason about structure.
Use patterns like:
API_KEY=REDACTEDuser_email=<EMAIL>customer_id=<UUID>s3://<BUCKET_NAME>/<PATH>When sharing logs, strip headers, query strings, and payloads. When sharing code, remove credentials and environment configs and keep only the minimal snippet needed to reproduce the issue.
AI suggestions can include code that resembles public examples. Treat anything you didn’t write as potentially “borrowed.” Practical guardrails:
Keep it short enough that people will follow it:
One page is enough. The goal is to keep vibe coding fast—without turning speed into risk.
Vibe coding works best when the human stays “in the pilot seat” and the AI is treated like a fast, talkative assistant. The difference is rarely the model—it’s the communication habits that prevent drift, silent assumptions, and accidental scope creep.
Treat each chat or session as a single mini-project. Start with a clear objective and a boundary. If the goal changes, start a new thread so context doesn’t blur.
For example: “Add client-side validation to the signup form—no backend changes.” That sentence gives you a clean success condition and a stop line.
After any meaningful step—choosing an approach, updating a component, changing a dependency—write a two- to four-line summary. This locks in intent and makes it harder for the conversation to wander.
A simple summary should answer:
Before you merge (or even switch tasks), request a structured recap. This is a control mechanism: it forces the AI to surface hidden assumptions and gives you a checklist to verify.
Ask for:
If an AI suggestion influenced the code, keep the “why” close to the “what.” Store key prompts and outputs alongside pull requests or tickets so reviewers can understand the intent and reproduce the reasoning later.
A lightweight template you can paste into a PR description:
Goal:
Scope boundaries:
Key prompts + summaries:
Recap (files/commands/assumptions):
Verification steps:
These patterns don’t slow you down—they prevent rework by keeping the conversation auditable, reviewable, and clearly owned by the human.
Vibe coding shifts learning from “study first, build later” to “build, then study what just happened.” That can be a superpower—or a trap—depending on how teams set expectations.
For junior developers, the biggest win is feedback speed. Instead of waiting for a review cycle to learn that an approach is off, they can ask for examples, alternatives, and plain-language explanations on the spot.
Good use looks like: generating a small snippet, asking why it works, then rewriting it in their own words and code. The risk is skipping that last step and treating suggestions as magic. Teams can encourage learning by requiring a short “what I changed and why” note in pull requests.
Senior engineers benefit most on boilerplate and option-search. AI can quickly scaffold tests, wire up glue code, or propose multiple designs to compare. That frees seniors to spend more time on architecture, edge cases, and coaching.
Mentorship also becomes more editorial: reviewing the questions juniors asked, the assumptions baked into prompts, and the trade-offs selected—rather than only the final code.
If people stop reading diffs carefully because “the model probably got it right,” review quality drops and understanding thins out. Over time, debugging becomes slower because fewer teammates can reason from first principles.
A healthy norm is simple: AI accelerates learning, not replaces understanding. If someone can’t explain a change, it doesn’t ship—no matter how clean the output looks.
Vibe coding can feel productive even when it’s quietly creating risk: unclear intent, shallow tests, or changes that “seem fine” but aren’t. Measuring success means choosing signals that reward correctness and clarity—not just speed.
Before you ask the AI for a solution, write what “done” means in everyday terms. This keeps the conversation anchored to outcomes instead of implementation details.
Example acceptance criteria might include:
If you can’t describe success without mentioning classes, frameworks, or functions, you’re probably not ready to delegate code suggestions yet.
When code is suggested rather than authored line-by-line, automated checks become your first line of truth. A “good” vibe-coding workflow steadily increases the percentage of changes that pass checks on the first or second micro-iteration.
Common checks to rely on:
If these tools are missing, success metrics will be mostly vibes—and that won’t hold up over time.
Useful indicators of progress are observable in team habits and production stability:
If PRs are getting bigger, harder to review, or more “mystery meat,” the process is slipping.
Define categories that always require explicit human approval: auth, payments, data deletion, permissions, security settings, and core business logic. The AI can propose; a person must confirm the intent and the risk.
“Good” in practice means the team ships faster and sleeps better—because quality is continuously measured, not assumed.
Vibe coding works best when you treat it like a lightweight production process, not a chat that “somehow” becomes software. The goal is to keep the conversation concrete: small scope, clear success criteria, and fast verification.
Pick a project you can finish in a day or two: a tiny CLI tool, a simple internal dashboard widget, or a script that cleans a CSV.
Write a definition of done that includes observable outcomes (outputs, error cases, and performance limits). Example: “Parses 10k rows in under 2 seconds, rejects malformed lines, produces a summary JSON, and includes 5 tests.”
A repeatable structure reduces drift and makes reviews easier.
Context:
- What we’re building and why
Constraints:
- Language/framework, style rules, dependencies, security requirements
Plan:
- Step-by-step approach and file changes
Code:
- Provide the implementation
Tests:
- Unit/integration tests + how to run them
If you want a deeper guide to prompt structure, keep a reference page for your team (e.g., /blog/prompting-for-code).
Use this after every iteration:
Ask for the next smallest change (one function, one endpoint, one refactor). After each step, run tests, skim diffs, and only then request the next iteration. If the change grows, pause and restate constraints before continuing.
If your goal is to make this workflow repeatable across a team, it helps to use tooling that bakes in guardrails: Koder.ai, for example, pairs chat-driven building with a structured planning flow and practical delivery features like source export and deployment/hosting—so the “conversation” stays connected to runnable software instead of becoming a pile of snippets.
“Vibe coding” is building software through an iterative conversation with an AI: you describe intent and constraints, the AI drafts code and explains trade-offs, and you run/inspect/test the result before asking for the next small change.
A practical definition is: prompts → code → verification → refinement, repeated in tight loops.
A spec tries to eliminate ambiguity up front; vibe coding uses ambiguity to discover requirements by seeing working output quickly.
Use vibe coding when you need fast exploration (UI flows, integrations, common patterns). Use specs when the cost of being wrong is high (payments, permissions, compliance) or when multiple teams need a stable contract.
Start with:
Then ask the AI to before it writes code; correct any drift immediately.
Keep each iteration small and testable:
Avoid “build the entire feature” prompts until you’ve proven the thin slice works.
Use three “hats”:
Even if the AI writes most lines, humans keep ownership of correctness and risk.
Ask for:
If you can’t explain the code path end-to-end after one or two rounds, simplify the approach or pause and consult docs.
Use a quick acceptance rule:
Practically: require at least one automated check (unit/integration test, typecheck, or lint) for each meaningful change, and verify unfamiliar APIs against official docs.
Common failure modes include:
Treat surprising additions (new dependencies, caches, queues) as hypotheses and require justification plus verification.
Don’t send:
Use placeholders like API_KEY=REDACTED and share the smallest reproducible snippet/log with headers and payloads removed.
Track signals that reward correctness and clarity, not just speed:
Add explicit human sign-off for high-impact areas (auth, payments, permissions, data deletion), even if the AI drafted the code.