Application Development as a Living Conversation with AI

Why Application Development Is Becoming a Conversation

Building software has always been a back-and-forth: a product owner explains a need, a designer sketches an approach, an engineer asks “what if?”, and everyone negotiates what “done” means. Calling it a conversation is useful because it highlights what actually drives progress—shared understanding—rather than any single artifact (a spec, a diagram, or a ticket).

Conversation turns ideas into intent

Most projects don’t fail because no one can write code; they fail because people build the wrong thing, or build the right thing on the wrong assumptions. Dialogue is how intent gets clarified:

• Goals: what outcome are we trying to create?
• Constraints: budget, time, compliance, existing systems, performance limits.
• Tradeoffs: speed vs. polish, flexibility vs. simplicity, cost vs. reliability.

A good conversation makes these explicit early, and revisits them as reality changes.

What changes when AI joins the team (and what doesn’t)

AI adds a new kind of participant—one that can draft, summarize, propose options, and generate code quickly. That changes the tempo of work: questions get answered faster, and prototypes appear sooner.

What doesn’t change is responsibility. Humans still decide what to build, what risks are acceptable, and what quality means for users. AI can suggest, but it can’t own consequences.

A preview of the workflow we’ll walk through

This post follows the conversation end to end: defining the problem, turning requirements into examples, iterating on design, making architecture decisions, co-writing and reviewing code, testing with shared definitions of “works,” keeping documentation current, and learning from real-world feedback after release—with practical guardrails for trust, safety, and quality along the way.

The New Team: Humans, AI, and Clear Responsibilities

Application development is no longer just a handoff from “the business” to “engineering.” The team now includes an additional participant: AI. That changes the pace of work, but it also makes role clarity more important than ever.

Who participates (and why they matter)

A healthy delivery team still looks familiar: product, design, engineering, support, and customers. What’s different is how often they can “be in the room” together—especially when AI can quickly summarize feedback, draft alternatives, or translate between technical and non-technical language.

Customers contribute lived reality: what’s painful, what’s confusing, what they will actually pay for. Support brings the unglamorous truth of recurring issues and edge cases. Product frames goals and constraints. Design turns intent into usable flows. Engineering ensures feasibility, performance, and maintainability. AI can support each of these conversations, but it does not own them.

What each party contributes

Humans provide context, judgment, and accountability. They understand trade-offs, ethics, customer relationships, and the messy details of the organization.

AI contributes speed and pattern recall. It can draft user stories, propose UI variants, suggest implementation approaches, surface common failure modes, and generate test ideas in minutes. It’s especially useful when the team needs options—not decisions.

Defining AI roles without giving up ownership

AI can be deliberately assigned “hats,” such as:

• Advisor: proposes approaches and risks to consider
• Drafter: produces first-pass specs, code, and copy
• Critic: challenges assumptions and reviews for gaps
• Tester: generates test cases and explores edge behavior
• Documenter: turns decisions into living notes and examples

To avoid “AI as the boss,” keep decision rights explicit: humans approve requirements, accept designs, merge code, and sign off releases. Treat AI output as a draft that must earn trust through review, tests, and clear reasoning—not confidence in its tone.

In practice, this is where “vibe-coding” platforms can help: a structured chat workflow makes it easier to keep intent, constraints, drafts, and revisions in one place—while still enforcing human approvals at the right gates.

From Ideas to Intent: Defining the Problem Together

Many projects start with a feature list: “We need a dashboard, notifications, and payments.” But features are guesses. A better starting point—especially when you have AI in the room—is a clear problem statement that explains who is struggling, what’s happening today, and why it matters.

Start with the problem, not the wishlist

Instead of asking an AI tool, “Build me a task app,” try: “Our support team loses time because customer requests arrive in five places and nothing is tracked end-to-end.” That single sentence gives direction and limits. It also makes it easier for humans and AI to propose solutions that fit the situation, not just common patterns.

Capture constraints early (so suggestions stay realistic)

AI will happily generate options that ignore your real-world boundaries unless you name them. Write down the constraints you already know:

• Budget and timeline (what’s fixed, what’s flexible)
• Compliance and security requirements (e.g., GDPR, SOC 2 expectations)
• Platforms and integrations (web/mobile, SSO, payment provider, internal tools)

These constraints aren’t “negative.” They’re design inputs that prevent rework.

Turn fuzzy goals into testable outcomes

“Improve efficiency” is hard to build toward. Convert it into success metrics you can measure:

• Reduce time-to-resolution from X to Y
• Increase self-serve completion rate to Z%
• Cut manual data entry steps from A to B

When outcomes are testable, AI can help generate acceptance examples and edge cases that align with your definition of success.

When a one-page brief beats brainstorming

Before asking for solutions, write a one-page brief: problem statement, users, current workflow, constraints, and success metrics. Then invite AI to challenge assumptions, propose alternatives, and list risks. That sequence keeps the conversation grounded—and saves days of “building the wrong right thing.”

Requirements as Dialogue: User Stories, Examples, and Clarity

Requirements work best when they read like a conversation: clear intent, shared understanding of what “done” means, and a few concrete examples. AI can accelerate this—if you treat it like a drafting partner, not an oracle.

Ask AI for user stories and acceptance criteria

Instead of “write requirements for feature X,” give the AI a role, constraints, and the audience. For example:

• “Propose 6 user stories for busy first-time users configuring notifications. Include acceptance criteria in plain English.”
• “Include one story for admin oversight, one for accessibility, and one for data export.”

Then review what it returns and edit ruthlessly. Keep stories small enough to build in days, not weeks. If a story includes multiple goals (“and also…”), split it.

Use examples to remove ambiguity

A user story without examples is often a polite guess. Add real scenarios:

• Typical flow: “A user signs up, chooses ‘Weekly summary,’ and receives it every Monday at 9am in their time zone.”
• Edge case: “User changes time zone on Sunday night—does delivery shift immediately or next cycle?”
• Failure state: “Email provider rejects the message—what does the user see, and what gets retried?”

You can ask AI to generate example tables and then validate them with your team: “List 10 examples, including 3 edge cases and 2 failure states. Mark any assumptions you had to make.”

Lightweight, but unambiguous

Aim for “thin but testable.” One page of crisp rules beats ten pages of vague prose. If something affects billing, privacy, or user trust, write it down explicitly.

Create a shared glossary

Misunderstandings often come from words, not code. Maintain a small glossary—ideally in the same place as your requirements:

• What’s the difference between a “workspace,” “account,” and “organization”?
• Does “member” include guests?
• What does “archived” mean: hidden, read-only, or deleted?

Feed that glossary back into your AI prompts so drafts stay consistent—and your team stays aligned.

Design in Loops: Rapid Iteration Without Rushing

Good design rarely arrives fully formed. It sharpens through loops: sketch, test, adjust, and repeat—while keeping the original intent intact. AI can make these loops faster, but the goal isn’t speed for its own sake. The goal is learning quickly without skipping the thinking.

Co-designing flows, wireframes, and microcopy

Start with the flow, not the screens. Describe the user’s goal and constraints (“a first-time user on mobile, one hand, low attention”), then ask AI to propose a few flow options. From there, use it to rough out wireframe-level layouts and draft microcopy variants (button labels, error messages, helper text) that match your brand voice.

A useful rhythm is: human defines the intent and tone, AI generates options, human selects and edits, AI tightens consistency across screens.

Multiple options, clear tradeoffs

When you ask for “three different approaches,” require tradeoffs, not just variations. For example: “Option A minimizes steps, Option B reduces user anxiety, Option C avoids collecting sensitive data.” Comparing tradeoffs early prevents the team from polishing a design that’s solving the wrong problem.

Accessibility and inclusivity early (not as a cleanup task)

Before anything feels “final,” run quick checks: color contrast assumptions, keyboard navigation expectations, readable error states, inclusive language, and edge cases like screen readers. AI can flag likely issues and propose fixes, but a human still decides what’s acceptable for your users.

Turning feedback into revisions without losing the “why”

Feedback is often messy: “This feels confusing.” Capture the underlying reason in plain language, then turn it into specific revisions (“rename this step,” “add a preview,” “reduce choices”). Ask AI to summarize feedback into a short change list tied to the original goal, so iterations stay aligned instead of drifting.

Architecture as Negotiation: Decisions, Not Decrees

Architecture used to be treated like a one-time blueprint: pick a pattern, draw a diagram, enforce it. With AI in the room, it works better as a negotiation—between product needs, delivery speed, long-term maintenance, and what the team can actually support.

Use AI to generate options, not orders

A practical approach is pairing human architecture decisions with AI-generated alternatives. You set the context (constraints, team skill level, expected traffic, compliance needs), and ask the AI to propose 2–3 viable designs with trade-offs.

Then you do the human part: choose what aligns with the business and the team. If an option is “cool” but increases operational complexity, say so and move on.

Draw boundaries early—then revisit them

Most architecture problems are boundary problems. Define:

• Modules and ownership (what belongs together, what doesn’t)
• APIs and contracts (inputs/outputs, error behavior)
• Data models (source of truth, migrations, analytics needs)
• Permissions and roles (who can do what, and why)

AI can help spot gaps (“What happens if the user is deleted?”), but boundary decisions should remain explicit and testable.

Keep a simple decision log

Maintain a lightweight decision log that records what you chose, why, and when you’ll revisit it. Think a short note per decision, stored near the codebase (e.g., /docs/decisions).

This prevents architecture from becoming folklore—and makes AI assistance safer, because the system has written intent to reference.

Fight overengineering with one question

When debates start spiraling, ask: “What is the simplest version that meets today’s requirements and won’t block tomorrow?” Have the AI propose a minimum viable architecture and a scale-ready upgrade path, so you can ship now and evolve with evidence later.

Coding as Co-Writing: Draft, Review, Refine

Treat AI like a fast junior teammate: great at producing drafts, not accountable for the final shape. Humans should steer architecture, naming, and the “why” behind decisions, while AI accelerates the “how.” The goal isn’t to outsource thinking—it’s to shorten the distance between intent and a clean, reviewable implementation.

A practical loop: draft → critique → tighten

Start by asking for a small, testable slice (one function, one endpoint, one component). Then immediately switch modes: review the draft for clarity, consistency, and fit with your existing conventions.

A useful set of prompt patterns:

• Generate: “Generate a POST /invoices handler using our existing validation helper and repository pattern.”
• Refactor: “Refactor this to remove duplication and keep side effects at the edges.”
• Explain: “Explain the control flow and where errors are handled. What assumptions are being made?”
• Add tests: “Add unit tests for success + validation failure + repository error, matching our test style.”

Keep the code readable on purpose

AI can produce correct code that still feels “off.” Keep humans in charge of:

• Naming that matches your domain language (not generic data/item).
• Comments that capture intent and tradeoffs, not restating the obvious.
• Consistent conventions (folder structure, lint rules, error handling).

If you maintain a short style snapshot (a few examples of preferred patterns), include it in prompts to anchor outputs.

Unblock, don’t bypass review

Use AI to explore options and fix tedious issues quickly, but don’t let it skip your normal review gates. Keep pull requests small, run the same checks, and require a human to confirm behavior against requirements—especially around edge cases and security-sensitive code.

If you want this “co-writing” loop to feel natural, tools like Koder.ai make the conversation itself the workspace: you chat to plan, scaffold, and iterate, while still keeping source control discipline (reviewable diffs, tests, and human approvals). It’s particularly effective when you want fast prototypes that can mature into production code—React for web, Go + PostgreSQL on the backend, and Flutter for mobile—without turning your process into a pile of disconnected prompts.

Testing as a Shared Language: Proving It Works

Testing is where a conversation becomes concrete. You can debate intent and design for days, but a good test suite answers a simpler question: “If we ship this, will it behave the way we promised?” When AI helps write code, tests become even more valuable because they anchor decisions in observable outcomes.

Turn acceptance criteria into test cases

If you already have user stories and acceptance criteria, ask AI to propose test cases directly from them. The useful part isn’t volume—it’s coverage: edge cases, boundary values, and “what if the user does something unexpected?” scenarios.

A practical prompt is: “Given these acceptance criteria, list test cases with inputs, expected outputs, and failure modes.” This often surfaces missing details (timeouts, permissions, error messages) while it’s still cheap to clarify.

Generate unit tests, sample data, and negative tests

AI can draft unit tests quickly, along with realistic sample data and negative tests (invalid formats, out-of-range values, duplicate submissions, partial failures). Treat these as a first draft.

What AI is especially good at:

• Producing consistent fixtures and mock objects
• Enumerating failure paths humans forget to write down
• Translating a spec into repeatable assertions

Keep humans responsible for risk and reality

Humans still have to review tests for correctness and real-world behavior. Is the test actually verifying the requirement—or just re-stating the implementation? Are we missing privacy/security scenarios? Are we checking the right level (unit vs integration) for this risk?

Bake it into your definition of done

A strong definition of done includes more than “tests exist.” It includes: passing tests, meaningful coverage of acceptance criteria, and updated docs (even if it’s a short note in /docs or a changelog entry). That way, shipping isn’t a leap of faith—it’s a proven claim.

Documentation That Stays Alive: Explain, Record, Reuse

Most teams don’t hate documentation—they hate writing it twice, or writing it and watching it drift out of date. With AI in the loop, documentation can shift from “extra work after the fact” to “a byproduct of every meaningful change.”

Explain: Turn decisions into readable notes

When a feature is merged, AI can help translate what changed into human-friendly language: changelogs, release notes, and short user guides. The key is to feed it the right inputs—commit summaries, pull request descriptions, and a quick note about why the change was made—then review the output like you would review code.

Instead of vague updates (“improved performance”), aim for concrete statements (“faster search results when filtering by date”) and clear impact (“no action needed” vs “reconnect your account”).

Record: Build internal docs that answer real questions

Internal docs are most useful when they match the questions people ask at 2 a.m. during an incident:

• Setup instructions that assume nothing and include common pitfalls
• Runbooks with “if this, then that” steps
• Troubleshooting guides based on actual tickets and incidents

AI is great at drafting these from existing material (support threads, incident notes, configuration files), but humans should validate the steps on a fresh environment.

Reuse: Keep docs in sync by making updates part of the change

The simplest rule: every product change ships with a doc change. Add a checklist item in pull requests (“Docs updated?”) and let AI suggest edits by comparing old and new behavior.

When helpful, link readers to supporting pages (for example, /blog for deeper explanations, or /pricing for plan-specific features). That way, documentation becomes a living map—not a forgotten folder.

Shipping and Learning: Continuous Feedback After Release

Shipping isn’t the end of the conversation—it’s when the conversation gets more honest. Once real users touch the product, you stop guessing how it behaves and start learning how it actually fits into people’s work.

Production is a feedback channel

Treat production like another input stream, alongside discovery interviews and internal reviews. Release notes, changelogs, and even “known issues” lists signal that you’re listening—and they give users a place to anchor their feedback.

Collect signals, then connect them

Useful feedback rarely arrives in one neat package. You’ll typically pull it from a few sources:

• Support tickets and chat transcripts (what’s painful)
• Analytics (what’s happening at scale)
• User interviews (why it’s happening)

The goal is to connect these signals into a single story: which problem is most frequent, which is most costly, and which is most fixable.

Let AI do the first pass—humans decide

AI can help summarize weekly support themes, cluster similar complaints, and draft a prioritized list of fixes. It can also propose next steps (“add validation,” “improve onboarding copy,” “instrument this event”) and generate a short spec for a patch.

But prioritization is still a product decision: impact, risk, and timing matter. Use AI to reduce the reading and sorting—not to outsource judgment.

Safe rollouts: small releases with an exit

Ship changes in a way that keeps you in control. Feature flags, staged rollouts, and fast rollbacks turn releases into experiments rather than bets. If you want a practical baseline, define a revert plan alongside every change, not after a problem shows up.

This is also where platform features can materially reduce risk: snapshots and rollback, audit-friendly change history, and one-click deploys turn “we can always revert” from a hope into an operational habit.

Trust, Safety, and Quality: Guardrails for AI Collaboration

Working with AI can speed up development, but it also introduces new failure modes. The goal isn’t to “trust the model” or “distrust the model”—it’s to build a workflow where trust is earned through checks, not vibes.

Common risks to plan for

AI can hallucinate APIs, libraries, or “facts” about your codebase. It can also smuggle in hidden assumptions (e.g., “users are always online,” “dates are in UTC,” “English-only UI”). And it may generate brittle code: it passes a happy-path demo but fails under load, odd inputs, or real data.

A simple habit helps: when AI proposes a solution, ask it to list assumptions, edge cases, and failure modes, then decide which ones become explicit requirements or tests.

Data privacy: what not to paste into prompts

Treat prompts like a shared workspace: don’t paste passwords, API keys, private customer data, access tokens, internal incident reports, unreleased financials, or proprietary source code unless your organization has approved tools and policies.

Instead, use redaction and synthesis: replace real values with placeholders, describe schemas rather than dumping tables, and share minimal snippets that reproduce the issue.

If your organization has data residency constraints, ensure your tooling can comply. Some modern platforms (including Koder.ai) run on globally distributed infrastructure and can deploy apps in different regions to help meet data privacy and cross-border transfer requirements—but policy still comes first.

Bias, fairness, and user impact

User-facing features can encode unfair defaults—recommendations, pricing, eligibility, moderation, even form validation. Add lightweight checks: test with diverse names and locales, review “who might be harmed,” and ensure explanations and appeal paths where decisions affect people.

Practical guardrails that actually work

Make AI output reviewable: require human code review, use approvals for risky changes, and keep an audit trail (prompts, diffs, decisions). Pair this with automated tests and linting so quality isn’t negotiable—only the fastest path to it is.

What the Next 3–5 Years Could Look Like (Without Hype)

AI won’t “replace developers” so much as it will redistribute attention. The biggest change is that more of the day will be spent clarifying intent and verifying outcomes, while less time goes to routine translation work (turning obvious decisions into boilerplate code).

Roles shift toward intent, UX, and verification

Expect product and engineering roles to converge around clearer problem statements and tighter feedback loops. Developers will spend more time:

• pressure-testing assumptions (“What happens when this rule conflicts with that one?”)
• shaping UX details (“What does ‘undo’ mean here, exactly?”)
• verifying behavior with examples, tests, and monitoring

Meanwhile, AI will handle more first drafts: scaffolding screens, wiring endpoints, generating migrations, and proposing refactors—then handing the work back for human judgment.

New skills that matter

Teams that get value from AI tend to build communication muscle, not just tooling. Useful skills include:

• Prompt writing as specification: asking for outputs with constraints, examples, and edge cases
• Critique and evaluation: spotting confident-but-wrong suggestions, checking for missing requirements
• Domain modeling: naming concepts well so both humans and AI stay consistent (entities, states, rules)

These are less about clever prompts and more about being explicit.

A repeatable conversation protocol

High-performing teams will standardize how they “talk to the system.” A lightweight protocol might be:

1. State intent (goal, users, non-goals)
2. Provide examples (happy path + edge cases)
3. Ask for options (trade-offs, risks, assumptions)
4. Decide (what you’ll do now vs later)
5. Verify (tests, checks, acceptance criteria)
6. Record (short notes in /docs so the next iteration starts informed)

Where AI helps most today—and what’s next

Right now, AI is strongest at accelerating drafts, summarizing diffs, generating test cases, and suggesting alternatives during review. Over the next few years, expect better long-context memory inside a project, more reliable tool use (running tests, reading logs), and improved consistency across code, docs, and tickets.

The limiting factor will still be clarity: teams that can describe intent precisely will benefit first. The teams that win won’t just have “AI tools”—they’ll have a repeatable conversation that turns intent into software, with guardrails that make speed safe.

If you’re exploring this shift, consider trying a workflow where conversation, planning, and implementation live together. For example, Koder.ai supports chat-driven building with planning mode, source export, deployment/hosting, custom domains, and snapshots/rollback—useful when you want faster iteration without giving up control. (And if you publish learnings along the way, programs like Koder.ai’s earn-credits and referral options can offset costs while you experiment.)