Vibe Coding: Why AI-Assisted Coding Works Only If You Can Hand-Code (And How to 10x Your Output)

Vibe Coding Starts Where Hand-coding Mastery Begins

Vibe coding—AI-assisted coding that feels like you’re pair-programming with a tireless senior dev—works only if you already know how to hand-code. The better you understand system architecture, OOP or functional programming, database schema design for fast queries, and modern cloud infrastructure, the more these tools become rocket fuel rather than roulette. As the saying goes: garbage in, garbage out. If your prompts and mental models are fuzzy, your results will be too.

I’ve shipped multiple production systems by vibe coding: more than five websites with AWS SDK integrations (SES, SNS/pub-sub and friends), several Flutter apps, native iOS (Swift/SwiftUI), and Java/Android apps. My favourite daily drivers are Cursor and Claude Code (and code-capable OpenAI models), switching between Claude, OpenAI, and Gemini depending on the task.

A flagship example is receiptreaderai.app—developed in Cursor where the AI wrote ~90% of a now 50K+ lines Swift/SwiftUI codebase over 1–2 months of prompt–test–tune loops. That speed is astonishing—but it only happened because the architecture, data model, and constraints were crystal-clear.

This article is a practical, no-fluff guide to vibe coding: how to set up, how to prompt, when to trust, when to verify, and how to wire tools so a strong engineer can genuinely work 10x faster without sacrificing quality.

What Vibe Coding Is (and Isn’t)

• Vibe coding is: using AI models as context-aware collaborators to scaffold, refactor, generate, and reason about code within your existing architecture and standards.
• Vibe coding isn’t: clicking “Generate App” and praying. You drive; the model assists.

Key insight: tool efficacy scales with your craftsmanship. If you can hand-code, you tell the model what good looks like, catch subtle errors quickly, and iterate in tight loops. If you can’t, the model can still produce code, but you’ll spend more time debugging or, worse, shipping brittle systems.

The 3 Pillars That Make Vibe Coding Work

1) Architecture Clarity

Document your target architecture at two levels:

• High level: modules, services, data flows (e.g., “iOS app → API Gateway → Lambda → RDS Postgres; SES for transactional mail; SNS for events”).
• Code level: directory structure, naming conventions, DI strategy, error-handling policy, logging, and test layout.

Feed this into your editor’s AI “context” (Cursor rules / project notes). The more the model knows about the skeleton, the more on-spec its output will be.

2) Data model + Schema Rules

Fast apps begin with sound schemas. Encode:

• Tables/collections, key constraints, indexes.
• Performance expectations (“queries must be <150ms P95”).
• Privacy/PII boundaries and retention rules.

Ask the model to produce migration scripts, seed data, and query plans. For SQL, demand explain plans and index suggestions.

3) Coding Standards & Checklists

Create a short, non-negotiable checklist the AI must follow:

• Language standards (Swift 5.9 + SwiftUI, Kotlin DSL, TypeScript strict).
• Patterns (MVVM + Combine/Swift Concurrency, Clean Architecture modules).
• Testing (unit + snapshot + lightweight property tests).
• Observability (structured logs, correlation IDs).
• Security (no secrets in code; use AWS Secrets Manager/Parameter Store).

Paste this once into your workspace rules; refer to it in prompts.

Tooling Stack for Vibe Coding (and Why)

• Cursor – AI-first IDE that treats the model as a teammate: project-wide context, diffs, multi-file edits, and “/fix” flows.
• Claude Code – excellent for long-context reasoning and refactors.
• OpenAI code models – strong at pattern generalisation and incremental edits.
• Git hooks + CI – always gate AI output with formatting (Prettier/SwiftFormat), linting (ESLint/Detekt), typechecks, unit tests, and SAST (e.g., Bandit, Semgrep).
• Package managers – lockfiles and provenance (Swift Package Manager, Gradle, pnpm) to keep generations deterministic.

RELATED READING: A Practical Guide to Building Agents: Deep Dive Into OpenAI’s Best Practices

Prompt Patterns That Turn AI Into A Senior Pair-Programmer

Use these vibe coding prompt styles inside Cursor or your editor:

A. Spec-first micro-PRs

“You are my pair-programmer. Implement a minimal SwiftUI view OrderListView that lists orders from OrderService. Constraints: MVVM, async/await, dependency injected via init, error banner on failure, 100% unit tests for view model, accessibility labels. Output: a single diff touching only OrderListView.swift, OrderListViewModel.swift, OrderService.swift, and OrderListViewModelTests.swift.”

Why it works: tight scope + explicit artefacts + tests.

B. Refactor with Invariants

“Refactor EmailSender to use AWS SES v3 client with exponential back-off. Preserve public API. Add structured logging and map AWS throttling to EmailError.rateLimited. Provide diff and new tests.”

C. Schema-performance Loop

“Given this Postgres schema and the query below, propose indexes and rewrite the query for P95 < 150ms on 5M rows. Show EXPLAIN ANALYZE rationale and create a migration.”

D. Post-gen Verification

“Self-critique the previous diff against our rules (security, logs, errors, tests). List violations and fix via a new diff only.”

These keep vibe coding on rails: the model generates, but you enforce boundaries and verification.

Real Integrations: AWS SDK examples the AI Can Scaffold (and You Should Verify)

Sending email with AWS SES (Node.js, v3 SDK)

// emailSender.ts
import { SESClient, SendEmailCommand } from "@aws-sdk/client-ses";

const ses = new SESClient({ region: process.env.AWS_REGION });

export async function sendTransactionalEmail(to: string, subject: string, html: string) {
  const params = {
    Destination: { ToAddresses: [to] },
    Message: {
      Body: { Html: { Charset: "UTF-8", Data: html } },
      Subject: { Charset: "UTF-8", Data: subject },
    },
    Source: process.env.SES_FROM!,
  };
  await ses.send(new SendEmailCommand(params));
}

Prompt to expand: “Wrap with retry/back-off, add CloudWatch structured logs, and unit tests using a mocked SES client.”

Pub/Sub with SNS → SQS (TypeScript)

// publishEvent.ts
import { SNSClient, PublishCommand } from "@aws-sdk/client-sns";
const sns = new SNSClient({ region: process.env.AWS_REGION });

export async function publishOrderCreated(event: { orderId: string; userId: string }) {
  await sns.send(new PublishCommand({
    TopicArn: process.env.SNS_ORDER_CREATED!,
    Message: JSON.stringify(event),
    MessageAttributes: { schema: { DataType: "String", StringValue: "order.created.v1" } }
  }));
}

Prompt to extend: “Create an SQS consumer that validates schema, handles poison messages via DLQ, and adds idempotency keys.”

AI will happily scaffold this; your job is to verify IAM, retries, DLQs, metrics, and idempotency are correct for prod.

FEATURED AWS CASE STUDY: How Containers on AWS Supercharged Bulky Goods Deliveries

Case Study: 90% AI-Generated Swift/SwiftUI App (receiptreaderai.app)

For receiptreaderai.app, I used vibe coding in Cursor to:

1. Scaffold modules (data layer, services, feature views) with MVVM.
2. Iterate UI via small prompts: “adjust spacing, add accessibility labels, snapshot tests.”
3. Integrate services (receipts OCR, cloud sync, notifications).
4. Continuously refactor for performance (diffable data sources, concurrency tuning).
5. Ship in weeks: ~50K lines over 1–2 months, where AI produced most of the code but I wrote the spec, verified the design, and fixed edge cases.

Lessons for vibe coding:

• Small PRs win. The model does best with surgical diffs and explicit test expectations.
• Architecture guards velocity. Because the skeleton was set, large features became safe incremental steps.
• Your taste sets the ceiling. AI can churn code; you decide what “done and good” means.

Garbage In, Garbage Out: Avoiding Low-Quality Outputs

To keep vibe coding sharp:

• Write short specs first (problem, constraints, tests).
• Templatise prompts (feature PR, refactor PR, bug fix PR).
• Never accept code without tests—ask the AI to write them, then you edit to capture tricky edge cases.
• Automate verification: linters, type checks, unit tests, security scans.
• Traceability: require PR descriptions link to the prompt (paste it) and the test evidence.
• Security & privacy: restrict context that leaves your machine; redact secrets; use local models if needed.

When Vibe Coding Is A Bad Idea

• You don’t understand the domain yet. AI will confidently generate the wrong thing. Do spikes first.
• Compliance-heavy code where provenance matters; hand-craft critical paths and have legal sign-off.
• Absence of tests. If your repo has no tests, vibe coding increases risk; write tests first.

A 14-Day Plan To Operationalise Vibe Coding In Your Team

Day 1–2: Set architecture doc + coding rules in your editor’s AI settings (Cursor Rules).
Day 3–4: Create prompt templates (feature/refactor/bug). Add CI gates (format, lint, test, SAST).
Day 5–6: Pick one backlog item; ship via micro-PRs (≤200 lines).
Day 7: Retro: list where AI excelled/failed; tune rules.
Day 8–9: Add schema/performance prompts; require EXPLAIN for all new SQL.
Day 10–11: Wire AWS SES/SNS helpers; add integration tests.
Day 12: Introduce “self-critique then fix” prompts before opening PRs.
Day 13: Measure: PR cycle time, defects per PR, test coverage.
Day 14: Document your house style for vibe coding and roll to more teams.

Frequently Asked Questions

Conclusion: Vibe Coding Rewards Real Engineering

Vibe coding is a force multiplier, not a shortcut. It amplifies engineers who already understand how software really works: architecture, OOP/FP, database schemas for fast queries, cloud primitives, and delivery pipelines. When you bring that expertise to Cursor, Claude Code and other capable models—and you enforce quality with prompts, tests, and CI—AI becomes the ultimate accelerator. That’s how you ship a native Swift/SwiftUI app with 90% AI-generated code in a couple of months and still sleep well at night.

If you’ve spent years hand-coding, you’re primed to get the most from vibe coding. Tight specs, small diffs, ruthless verification, and the confidence to steer. The result isn’t just more code; it’s better systems shipped sooner.

---

Want to know more about AI-assisted coding?