Sep 30, 2025·8 min
The Future of Vibe Coding: Bigger Context, Smarter AI Tools
Explore how vibe coding may evolve as AI models improve, context windows expand, and tools become ambient—plus the skills, risks, and workflows teams need.
Explore how vibe coding may evolve as AI models improve, context windows expand, and tools become ambient—plus the skills, risks, and workflows teams need.
“Vibe coding” is a style of building software where you start with intent—what you want the program to do—and let an AI help turn that intent into working code. Instead of writing every line from scratch, you steer: you describe the behavior, constraints, and examples, then review what the tool produces, edit it, and iterate.
The key idea is that the unit of work shifts from “type code” to “direct and verify.” You’re still responsible for the outcome, but you spend more time shaping requirements, choosing trade-offs, and checking results.
Vibe coding is:
It’s not just autocomplete. Autocomplete predicts the next few tokens based on local context; vibe coding aims to generate or transform larger chunks based on your stated intent.
It’s not templates. Templates stamp out a known pattern; vibe coding can adapt a pattern to a new situation and explain choices (even if you should still verify them).
It’s not no-code. No-code tools abstract code away behind UI builders. Vibe coding still produces and edits code—often faster—but you remain in the codebase.
It shines in prototypes, “glue code” (connecting APIs, data formats, services), and refactors like renaming, reorganizing modules, or migrating from one library to another. It’s also useful for writing tests, docs, and small utilities—especially when you can provide examples of inputs and expected outputs.
It’s weaker on deep, multi-step bugs where the real cause is hidden in system behavior, timing, or missing domain knowledge. It also struggles when requirements are unclear or conflicting: if you can’t describe what “correct” looks like, the tool can’t reliably produce it.
In those moments, the job is less “generate code” and more “clarify intent,” with the AI supporting—not replacing—that thinking.
Vibe coding isn’t suddenly popular because developers forgot how to write code. It’s taking off because the cost of “trying an idea” has dropped sharply. When you can describe a change, get a working draft in seconds, and immediately test it, experimentation stops feeling like a detour and starts feeling like the default.
A lot of day-to-day development time is spent translating intent into syntax, wiring, and boilerplate—then waiting to see if it works. AI-assisted programming compresses that cycle into a tight loop:
That speed matters most for the unglamorous work: adding a new endpoint, refactoring a component, updating validations, writing a migration, or creating a quick script. These are “too small to plan heavily,” but they add up.
Teams are under pressure to ship outcomes, not just output. When AI can draft code quickly, attention moves toward clarifying product intent: what should happen for the user, what trade-offs are acceptable, and how the system should behave under real-world conditions.
This is especially noticeable in early-stage projects, internal tools, and iterative product work where requirements change weekly.
The big change isn’t only model quality—it’s integration. Assistance is increasingly available where decisions happen: inside the editor, in code review, in tests, and in debugging. That reduces the “context switching tax” of copying snippets between tools.
As generating becomes cheap, verifying becomes the hard part. The teams benefiting most treat AI output as a draft—then validate with tests, careful reviews, and a clear definition of “done.”
Early AI coding tools mostly behaved like autocomplete: they helped you type faster, but you still had to “drive.” As models improve, they start acting less like a suggestion box and more like a collaborator that can carry a task from intent to implementation.
Newer models are increasingly capable of handling multi-step work: planning changes, making several related edits, and keeping track of why each step matters.
In practice, that means you can ask for outcomes (“Add a billing tier and update the checkout flow”) instead of micromanaging every line. The model can propose a sequence: update data structures, adjust UI, change validation rules, and add tests.
The limit is that “better” doesn’t mean “unbounded.” Long chains of dependent decisions still break if requirements are unclear or the codebase has hidden constraints. You’ll feel the improvement most on tasks with crisp goals and well-defined interfaces.
Models perform best when you provide concrete constraints: inputs/outputs, acceptance criteria, edge cases, and non-goals. When you do, code generation becomes noticeably more consistent—fewer missing cases, fewer mismatched names, fewer invented APIs.
A useful mental model: the model is great at executing a clear spec, but mediocre at guessing one.
A big shift is moving from “generate a new file” to “safely modify what’s already there.” Improved models are better at:
This is where the experience starts to feel like “decisions” rather than “suggestions”: you delegate a change request, and the tool returns a coherent set of diffs that fit the project’s style.
Even as models get smarter, a core risk remains: they can sound certain while being wrong. The failure mode becomes subtler—fewer obvious syntax errors, more “looks plausible but violates a rule” mistakes.
So the human role shifts from typing code to validating decisions. Instead of asking, “Did it compile?” you’ll ask, “Is this the right behavior?” and “Does this respect our security and business constraints?”
The payoff is speed. The price is a new kind of vigilance: treating AI output as a strong draft that still needs review, tests, and clear acceptance checks before it counts as done.
A “context window” is simply how much information an AI model can hold in working memory while it writes or edits code. A useful analogy: imagine asking a contractor to renovate your house. With a small context window, you can only show them one room at a time—so they might paint beautifully, but accidentally block a doorway that connects to the next room. With a larger context window, they can walk through the whole house and understand how a change in the kitchen affects the plumbing in the basement.
When an AI can “see” more of your repository at once—core modules, shared utilities, API contracts, tests, and documentation—it can make edits that line up across the codebase instead of producing isolated fixes.
That shows up in practical ways:
In other words, a bigger context window nudges AI assistance from “help me write this function” toward “help me change this system without breaking it.”
Even if models can ingest an entire repo, they still won’t automatically know what isn’t written down.
So “whole-codebase understanding” is not the same as “whole-product understanding.” Teams will still need humans to provide goals, constraints, and context that isn’t encoded.
As context windows grow, the bottleneck becomes less about token limits and more about signal quality. If you feed the model a messy, contradictory pile of files, you’ll get messy, contradictory changes.
Teams that benefit most will treat context as an asset:
The future isn’t just bigger context—it’s better context, intentionally packaged so the AI is looking at the same source of truth your best developers rely on.
The biggest shift won’t be a “better chat window.” It’ll be AI help embedded across the places you already work: the editor, the terminal, the browser, and even your pull requests. Instead of asking for help and then copy‑pasting results back into your workflow, suggestions will surface where the decision is happening.
Expect AI to follow you through the whole loop:
Ambient tools will increasingly do the scavenger hunt for you: pulling the right files, configuration, tests, ADRs, and prior PR discussions into the moment. Instead of “here’s an answer,” the default will be “here’s the evidence”—the exact code references and past decisions the suggestion is based on.
That retrieval layer is what makes assistance feel “invisible”: you don’t ask for context; it arrives with the recommendation.
The most useful help will be quiet and specific:
Ambient help can turn into noise—popups, auto-edits, and competing recommendations that break focus. Teams will need good controls: adjustable “quiet modes,” clear confidence signals, and policies about when auto-changes are allowed versus when the tool must ask first.
Vibe coding shifts the center of gravity from “write code, then explain it” to “state intent, then shape the result.” The keyboard doesn’t disappear—but a bigger share of your time moves to defining what you want, checking what you got, and steering the tool with clear feedback.
Instead of jumping into files, many developers will begin by writing a short “work order” for the AI: the goal, constraints, and acceptance criteria. Think: supported inputs, performance limits, security boundaries, and what a correct result looks like.
A good prompt often reads like a mini spec:
One-shot prompts that rewrite a whole feature will feel increasingly risky—especially in shared codebases. The healthier rhythm is: ask for a small change, run tests, review the diff, then move to the next step.
This keeps you in control and makes rollbacks trivial. It also makes reviews easier because each change has a clear purpose.
A simple habit will save hours: ask the tool to restate the task and plan first. If it misunderstood your constraint (“don’t change the public API”) or missed a key edge case, you find out before any code is generated.
This step turns prompts into a two-way conversation, not a vending machine.
As AI touches more files, teams will benefit from a short, consistent record:
Over time, this becomes the glue between intent, code review, and debugging—especially when the “author” is partly an agent.
Vibe coding shifts the center of gravity from “typing the right syntax” to steering an AI-assisted programming process. As models and context windows improve, your leverage increasingly comes from how well you define the problem—and how quickly you can verify the result.
A useful mental model is moving from “write code” to “design constraints and validate outcomes.” Instead of starting with implementation details, you’ll spend more time specifying:
This is how you keep agentic coding tools aligned when they make many small decisions on your behalf.
As ambient IDE assistance makes generating code cheap, debugging becomes the differentiator. When AI output fails, it often fails plausibly—close enough to pass a skim, wrong enough to cause subtle bugs. Strong developers will be the ones who can:
That’s system thinking: understanding how pieces interact, not just how functions compile.
Prompting for developers will matter, but not as clever tricks. The high-leverage approach is clarity: define scope, provide examples, name constraints, and describe failure modes. Treat prompts like mini specs—especially for AI-assisted programming tasks that touch multiple modules.
The healthiest habit in a human-in-the-loop workflow is assuming the model produced a strong first draft, not a final answer. Review it like you would a junior teammate’s PR: check correctness, security boundaries, and maintainability.
Vibe coding can feel like magic: you describe the intent, the tool produces working-looking code, and you keep moving. The risk is that “working-looking” isn’t the same as correct, secure, or maintainable. As AI assistance becomes more frequent—and more automatic—the cost of small mistakes compounds quickly.
Generated code is often plausible but wrong. It may compile, pass a happy-path manual check, and still fail under real-world conditions: edge cases, concurrency, unusual inputs, or integration quirks. Worse, the code can be wrong in a way that’s hard to notice—like silently dropping errors, using the wrong timezone, or “helpfully” changing behavior to match its guess of your intent.
The practical implication: velocity shifts from typing code to verifying behavior.
AI tools can accidentally widen your attack surface in a few common ways:
Guardrails here are as much about process as they are about technology.
Vibe-coded changes can degrade codebases in subtle ways:
These don’t always break production today—but they raise maintenance costs and make future changes harder.
The safest teams treat AI output as a draft that must earn its way into the codebase:
Vibe coding stays powerful when the “vibe” accelerates creativity—but verification protects users, systems, and teams.
A copilot suggests. An agent does.
That single shift changes the shape of work: instead of asking for snippets and then stitching them together yourself, you assign a goal (“upgrade this library across the repo” or “add tests for these endpoints”), and the tool plans steps, edits files, runs checks, and reports back with evidence.
Agentic tools act more like a junior teammate you can delegate to. You give a task with constraints, it breaks the job into smaller steps, tracks what it touched, and summarizes outcomes: what changed, what failed, what it couldn’t confidently decide.
Good agents also create paper trails: diffs, command output, and notes you can review quickly rather than re-deriving everything.
Agents tend to shine on work that’s tedious, repeatable, and easy to verify:
The key is that you can validate success with tooling: builds, tests, linters, snapshots, or a small set of known behaviors.
Even with better models, humans stay responsible for decisions that don’t have a single “correct” answer:
Agents can propose options, but you own the intent.
When a tool can take many steps, it can also wander. Prevent drift with structure:
Treat agent runs like mini-projects: bounded goals, observable progress, and clear stop conditions.
As AI helps write more of the code, teams will win or lose based on process. The technical output may be faster, but the shared understanding still has to be built—and that’s a team habit, not a model feature.
Pull requests will increasingly be bundles of generated changes. That makes “scan the diff and trust your instincts” less effective.
Expect PR templates to emphasize intent and risk: what the change is supposed to do, what could break, and how it was checked. Reviews will focus more on invariants (security rules, domain logic, performance constraints) and less on formatting or boilerplate.
Tickets may also become more structured: clear success criteria, edge cases, and sample inputs/outputs give both humans and tools a reliable target. A good ticket becomes the contract that keeps AI output on track.
High-performing teams will standardize a few lightweight artifacts that reduce ambiguity:
These aren’t paperwork—they’re memory. They prevent future rework when no one can explain why a generated pattern exists.
Teams will need explicit policies for:
Velocity alone is misleading. Track outcomes: lead time, escaped defects, production incidents, and maintainability signals (lint/error trends, complexity, flaky tests). If AI increases throughput but worsens these, the process—not the people—needs adjustment.
Vibe coding is moving from “help me write this function” to “help me steer this system.” The change won’t be a single breakthrough—it’ll be a steady blend of better models, longer context, and tools that feel less like a chatbot and more like an always-on teammate.
Expect fewer copy-paste moments and more “surgical” help: multi-file edits that actually compile, suggestions grounded in your repo’s conventions, and assistants that pull the right context (tests, docs, recent PRs) without you hand-feeding it.
You’ll also see more ambient assistance: inline explanations, automatic generation of small tests, and quicker code review support—still driven by you, but with less friction.
The big leap is refactoring and migration work: renames across a codebase, dependency upgrades, deprecations, performance cleanups, and “make it consistent” chores. These are ideal for agents—if the guardrails are real.
Look for workflows where the tool proposes a plan, runs checks, and produces a reviewable change set (a PR) rather than directly editing your main branch. The best teams will treat AI output like any other contribution: tested, reviewed, and measured.
Over time, more work starts from intent: “Add enterprise SSO with these constraints,” “Reduce p95 latency by 20% without raising cost,” or “Make onboarding take under 10 minutes.” The system turns that intent into a sequence of small, verified changes—continuously checking correctness, security, and regressions as it goes.
This doesn’t remove humans; it shifts humans toward defining constraints, evaluating trade-offs, and setting quality bars.
Start small and measurable. Pick a pilot where failures are cheap (internal tooling, test generation, docs, a contained service). Define success metrics: cycle time, defect rate, review time, and rollback frequency.
When evaluating tools, prioritize: repo-aware context retrieval, transparent change plans, strong diff/PR workflows, and integrations with your existing CI and security checks.
If you’re exploring “vibe coding” beyond the editor—especially for full applications—platforms like Koder.ai are a useful reference point for where tooling is heading: intent-first development in a chat interface, a planning mode for agreeing on scope before changes land, and safety features like snapshots and rollback. In practice, capabilities like source code export and reviewable changes (plus deployment/hosting options when you want them) reinforce the core lesson of this article: speed is real, but it only stays valuable when verification and control are built into the workflow.
Finally, invest in skills that compound: writing precise intent and constraints, creating good acceptance tests, and building verification habits (tests, linters, threat modeling) so AI speed doesn’t become AI debt.
Vibe coding is an intent-first workflow: you describe the behavior you want (plus constraints and examples), an AI drafts code, and you verify, edit, and iterate. The “unit of work” becomes directing and validating outcomes rather than typing every line.
It’s different from:
You’re still responsible for correctness, security, and maintainability. A practical stance is to treat AI output like a strong draft from a junior teammate: review assumptions, run tests, and confirm it matches your constraints and product intent.
It’s most effective for:
It struggles when:
In those cases, the highest leverage move is clarifying intent and isolating evidence before asking for code changes.
Because the cost of trying ideas has dropped: describe → generate → run → adjust. As generation becomes cheap, teams can iterate faster on small changes and experiments—especially the “unsexy” work like validations, endpoints, migrations, and refactors.
Ask for a small “work order” the AI can execute:
Then request an “explain back + plan” before it writes code to catch misunderstandings early.
Use a tight loop:
Avoid one-shot prompts that rewrite whole features unless you can easily rollback and thoroughly verify.
Because AI output can be plausible but wrong. Common failure modes include missed edge cases, invented APIs, silent behavior changes, and overconfident explanations. Verification—tests, reviews, and explicit acceptance checks—becomes the main bottleneck.
Use layered guardrails: