Learn how AI interprets plain-language instructions, plans UX flows, generates UI and code, and iterates with feedback to deliver working features and screens.

“Written instructions” are the words you already use to explain what you want built—captured in a form that an AI (and a team) can act on.
In practice, the goal isn’t perfect prose. It’s clear intent (what outcome you want) plus clear boundaries (what’s allowed, what’s not), so the system doesn’t have to guess.
These can be formal or informal:
The key is that the text describes outcomes and constraints. When both are present, AI can reliably propose screens, flows, and implementation details without inventing business rules.
A working feature is more than a mockup. Typically it includes:
For example, “saved addresses” isn’t just a page—it’s a set of screens (list, add/edit), rules (required fields, default address), and wiring (API calls, state updates).
Most teams end up in a simple cycle:
Describe → generate → review → refine
You provide the spec, the AI proposes UI/UX and implementation, you review for accuracy and product fit, then you refine requirements until the result matches what you meant.
If you use a vibe-coding platform like Koder.ai, this loop often becomes even tighter because you can stay in one place: describe the feature in chat, generate the app changes, then iterate quickly with targeted follow-ups (and revert if needed).
AI can speed up drafting screens, suggesting flows, and producing code, but people still:
Think of AI as an accelerator for turning text into a first (and second) draft—while humans remain responsible for the final result.
AI is flexible about formats, but picky about clarity. It can work from a single paragraph, a bullet list, a PRD snippet, or a set of user stories—as long as intent and constraints are explicit.
The most useful starting points usually include:
These elements tell the AI what you’re building and what ‘good’ looks like, which reduces back-and-forth.
When requirements are missing, the AI fills gaps with defaults that may not match your business rules. Include:
Vague: “Add a checkout screen and make it simple.”
Concrete: “Add a checkout flow for logged-in users. Steps: Address → Shipping → Payment → Review. Support card + Apple Pay. Save up to 3 addresses per user. Show tax and shipping before payment. If payment fails, keep the cart and show a retry option. Success = order created, receipt emailed, and inventory decremented.”
Clear inputs help the AI produce screens, copy, validation, and logic that align with real constraints. You get fewer mismatched assumptions, fewer redesign cycles, and a faster path from a first draft to something your team can actually review, test, and ship.
Before AI can generate screens or write code, it has to figure out what you mean, not just what you wrote. This step is essentially “reading” your spec like a product manager: extracting goals, the people involved, and the rules that make the feature correct.
Most specs contain a few recurring building blocks:
When these are clear, AI can translate text into a structured understanding that later steps can turn into flows, screens, data, and logic.
AI also recognizes common product patterns and maps everyday phrasing to implementation concepts. For example:
This mapping is useful because it turns vague nouns into concrete building blocks that designers and engineers use.
Even good specs leave gaps. AI can flag what’s missing and propose clarification questions like:
Sometimes you want progress even without answers. AI can choose reasonable defaults (e.g., standard password rules, typical dashboard widgets) while marking assumptions for review.
The key is visibility: assumptions should be listed clearly so a human can confirm or correct them before anything ships.
Once the intent is clear, the next move is to turn a written spec into something you can actually build: a feature plan. You’re not looking for code yet—you’re looking for structure.
A good plan starts by translating sentences into screens, navigation, and user journeys.
For example: “Users can save items to a wishlist and view it later” usually implies (1) a product detail interaction, (2) a wishlist screen, and (3) a way to reach it from the main nav.
Ask the AI to list the screens and then describe the “happy path” journey, plus a couple of common detours (not logged in, item removed, empty list).
Next, have the AI split the feature into tasks teams recognize:
This is also where vague requirements get exposed. If the spec doesn’t say what happens when a user tries to save the same item twice, the plan should surface that question.
Keep acceptance criteria in plain language. Example:
Ask the AI to label items as must-have vs nice-to-have (e.g., “share wishlist” might be nice-to-have). This prevents the plan from quietly expanding beyond the original spec.
With a feature plan in hand, AI can help turn text into a concrete “screen map” and an early UI draft. The goal isn’t pixel-perfect design on the first try—it’s a shared, inspectable model of what users will see and do.
Start by describing the happy path as a short story: what the user wants, where they start, what they tap, and what success looks like. From that, AI can propose the minimum set of screens (and what belongs on each).
Then ask for common alternatives: “What if they’re not logged in?”, “What if they have no results?”, “What if they abandon halfway?”. This is how you avoid building a UI that only works in demos.
If your spec includes layout hints (e.g., “header with search, results list with filters, primary CTA at the bottom”), AI can produce a structured draft such as:
The best prompts include content priorities (“show price and availability above description”), interaction rules (“filters persist across sessions”), and constraints (“mobile-first; works with one thumb”).
A working product needs more than the “normal” screen. Have AI enumerate and define the states you’ll implement:
These state decisions directly affect development effort and user trust.
AI can help enforce consistency by proposing reusable components and rules: type scale, spacing tokens, button styles, and form patterns.
If you already have components, link to your internal guidelines (e.g., /design-system) and ask the AI to reuse them rather than inventing new patterns.
Next, translate “what the app should do” into what the app should store and what it should allow. This is where written specs become a concrete data model and a set of business rules.
AI starts by pulling out the “nouns” and key concepts in your text and treating them as entities. For example, “Users can create Projects and add Tasks, and managers approve time entries” suggests entities such as User, Project, Task, and TimeEntry.
For each entity, AI suggests the fields you’ll need (and flags what’s missing):
It should also call out implied edge cases, like “Only one active subscription per account” (a uniqueness constraint) or “Order total must equal sum of line items” (a calculated validation).
Good output keeps rules readable, not buried in code. Examples:
Finally, map how records change over time: create, update, delete, and what to do instead of deleting (soft delete). AI can also propose audit trails (who changed what, when) and history/versioning when the spec needs traceability.
Now you can generate the “first working draft” of code: the UI people click, and the logic that makes it behave correctly.
If you’re using Koder.ai, this typically means the platform generates a coherent full-stack implementation (web, backend, database) from your chat-driven spec, with the option to export source code when you want to continue in a traditional workflow.
From a spec like “Add a ‘Create Project’ screen with name, owner, and visibility,” AI can scaffold:
It can also generate reusable building blocks (e.g., a <ProjectForm /> used for both create and edit), so the code stays consistent.
On the server side, AI can draft the basic “contract” for the feature:
The key is tying backend logic to the spec’s rules (“Only admins can set visibility to private”) rather than just saving whatever the UI sends.
AI can wire the UI to your API client (fetch/Axios/React Query, etc.), including caching and retries where appropriate. It should also generate user-friendly error handling: field-level messages for validation errors and a clear fallback for network failures.
// Example: submit handler with loading + error state
async function onSubmit(values) {
setStatus({ loading: true, error: null });
try {
await api.post('/api/projects', values);
router.push('/projects');
} catch (e) {
setStatus({ loading: false, error: 'Could not create project. Try again.' });
}
}
Generated code is most useful when it follows your conventions: clear naming, predictable folder structure, small functions, and shared utilities (validators, API clients, permission helpers).
If you have a style guide or preferred patterns, reference them explicitly and link to internal docs like /engineering/frontend or /engineering/api-guidelines.
By this point you have screens, UI components, data shapes, and business rules. “Wiring” is where those pieces actually talk to each other: buttons trigger actions, actions call backend endpoints, responses update the UI, and permissions decide what people can see.
AI can connect screens according to the written spec by creating routes (URLs or app paths), defining what happens after key actions, and passing the right context between pages.
For example: “After saving, return to the list and highlight the new item” becomes a concrete flow—submit form → await success → navigate to list → show a toast and focus the new row.
Specs often mention roles (“Admin can edit, Viewer can only read”). Wiring means enforcing that in more than one place:
AI is helpful here because it can generate consistent checks across the app (not just one screen), reducing the risk of “it looks locked, but the endpoint still works.”
Most features depend on configuration: API base URLs, analytics keys, feature flags, storage buckets, etc. AI can set up separate settings for dev/staging/prod while keeping secrets out of the codebase.
Typical outputs include:
.env templates (safe placeholders)The goal is a full loop: “click → request → response → UI update.” AI can add the missing glue code (loading states, error handling, retries) and generate simple checks such as:
This is where a feature stops being a mock and starts behaving like a real product.
Once a feature is “working,” test it the same way a real user (and a messy real world) will. AI helps by turning written acceptance criteria into concrete checks—and by speeding up the tedious parts of debugging.
If your spec says, “A user can reset their password and sees a confirmation message,” AI can propose test cases that match that statement at multiple levels:
The trick is to feed AI the exact acceptance criteria plus minimal context: feature name, key screens, and any existing test conventions in your codebase.
Specs usually describe the happy path. AI is useful for brainstorming the “what if” scenarios that cause support tickets:
You don’t need to implement every edge case immediately, but you should decide which ones matter for your product’s risk level.
When a test fails, give AI what a developer would ask for anyway: the failing assertion, relevant logs, stack traces, and exact reproduction steps.
AI can then:
Treat its suggestions as hypotheses. Confirm them by rerunning the test and checking behavior in the UI.
For quick review cycles, keep a short checklist:
The first AI-generated draft is usually “good enough to react to,” not “ready to ship.” Iteration is where you turn a plausible feature into a reliable one—by tightening requirements, correcting edge cases, and making changes in small, reviewable steps.
A healthy loop looks like: generate → review → ask for a specific change → compare what changed → repeat.
Instead of re-prompting for the entire app, aim for targeted updates. Ask the AI to modify only one piece (a screen, a component, a validation rule, a query) and return a diff or a clearly marked “before/after.” This makes it easier to confirm the change solved the issue without accidentally breaking something else.
If your workflow supports it, keep changes in small commits and review them like you would a teammate’s pull request: scan the diff, run the app, and verify the behavior.
Platforms like Koder.ai also benefit from this approach: use “planning mode” (or an equivalent step) to agree on scope and flows first, then generate, then iterate in narrow slices—and rely on snapshots/rollback when experimentation goes sideways.
Vague requests (“make it nicer,” “fix the flow”) create vague results. Strong change requests reference:
Add acceptance criteria when possible: “The ‘Pay’ button is disabled until required fields are valid” or “If shipping country changes, recalculate tax immediately.”
Treat AI output as code you own. Require short change notes alongside updates: what changed, why it changed, and what to test.
When an AI suggests refactors, ask it to explain the intent and list potential risks (for example, “this changes validation timing” or “this alters API response handling”).
Iteration ends when you hit clear release criteria. Define boundaries:
At that point, freeze the spec, ship, and plan the next iteration as a new, scoped change request.
AI can turn written specs into surprisingly complete features, but it’s not a substitute for judgment. Treat AI output as a draft that needs review—especially when it touches user data, payments, or permissions.
Assume anything you paste into a prompt could be stored or reviewed. Don’t include:
If you need realism, anonymize: replace names with placeholders, scramble IDs, and describe patterns (“10k users, 3 roles”) instead of raw exports.
AI is useful for generating baseline security checks, but you still need to verify them.
Before you ask for code or screens, include:
Once you have a draft prototype, schedule a quick review: compare it to your roadmap, decide what ships now vs later, and document changes.
If you want help turning drafts into a plan, see /pricing or browse related guides in /blog. If you’re exploring chat-driven development, Koder.ai is designed for this workflow: turn written specs into working web, backend, and mobile features, iterate quickly, and export the source code when you’re ready.
“Written instructions” are any text that clearly states intent (the outcome you want) and boundaries (constraints, rules, and what’s not allowed). That can be a quick Slack message, a PRD snippet, user stories, acceptance criteria, or a list of edge cases—what matters is clarity, not formality.
A “working” feature usually includes more than visuals:
A mockup shows appearance; a working feature behaves correctly end-to-end.
Most teams use a simple iteration loop:
The speed comes from quick drafts; the quality comes from disciplined review and iteration.
AI can move fast, but it will guess if you don’t specify:
Including these upfront reduces rework and prevents “reasonable defaults” that don’t match your business.
Start with four elements:
This gives the AI both direction and a quality bar, not just a feature idea.
Concrete specs define:
These specifics translate directly into screens, rules, and API behavior.
Ask the AI to produce a feature plan before code:
This exposes missing requirements early, when changes are cheap.
Request explicit definitions for each key screen state:
Most production bugs and UX issues come from missing state handling, not the happy path.
AI usually extracts entities (the “nouns”) and then proposes:
Have it also describe the data lifecycle: create/update/soft-delete and whether you need audit trails or history.
Treat AI output as a draft and set guardrails:
Use AI to accelerate iteration, but keep humans accountable for correctness, security, and quality.