• 5 min read • Namanyay Goel

The Complete Guide to Building Production Apps with AI (2025)

guideaiproductionnon-technicalplaybook

One handbook for vibe coders who want to ship real products with Cursor, Claude, and ChatGPT without hiring a full engineering crew.

AI turned shipping software into a solo sport. The risk: shipping a brittle product you don’t understand. This guide wraps everything we teach non-technical founders into one playbook.

Use it as a reference. Each stage links to deep dives across our content hub.

Part I: Foundations #

1. Set Your North Star #

  • Clarify the job to be done for users.
  • Write a one-page product brief covering audience, problem, and success metrics.
  • Define revenue or outcome targets for the first 90 days.

2. Choose Your AI Stack #

  • Cursor / Windsurf for local coding assistance.
  • Claude Sonnet / GPT-o1 for architectural reasoning.
  • ChatGPT / OpenAI GPT-4o for utilities, docs, and test scaffolding.
  • Giga for prompts, diagnostics, and guardrails.

3. Adopt the Three-Feature MVP Rule #

Pick the three features that prove your promise. Freeze the rest until those three work end to end. The simplest launch wins.

Part II: Building the First 10,000 Lines #

1. Scaffold Quickly, Document Immediately #

  • Use Cursor to generate Next.js scaffolds.
  • Document architecture in docs/architecture.md while it’s still small.
  • Record prompt snippets for future reference.

2. Install Early Guardrails #

Even in the honeymoon phase:

  • Add smoke tests for onboarding and billing.
  • Set up Sentry and Posthog.
  • Track prompt history in Notion or Git.

3. Create a Launch Definition #

Write down what “ready” means before you build. Include smoke tests, demo script, support plan, and the smallest success metrics you care about.

Part III: Scaling to 40,000 Lines #

1. Anticipate the Complexity Wall #

  • Schedule monthly “stability sprints.”
  • Version control your workspace prompts.
  • Plan a response for the moment AI starts touching files you didn’t mention.

2. Introduce Observability + Testing #

  • npm run smoke becomes mandatory.
  • Add contract tests per integration.
  • Review Sentry/Posthog dashboards daily.

3. Guard Against Drift #

  • Install .cursorrules with restricted directories.
  • Require evidence (“UNVERIFIED” if unsure) from AI assistants.
  • Add alerts for wide diffs and schema changes.

Part IV: Surviving 50,000+ Lines #

1. Freeze and Inventory #

  • Catalog routes, jobs, env vars, integrations.
  • Log every regression and assign an owner.

2. Rebuild Trust in Your Tools #

  • Rewrite workspace prompts with strict boundaries.
  • Pair AI-generated diffs with smoke tests.
  • Maintain a prompt diff log.

3. Triage Feature Creep #

  • Enforce feature flags and “Not Yet” lists.
  • Score every feature request on impact, confidence, and maintenance cost.

4. Budget for Recovery #

  • Allocate time/money for structured rescues.
  • Run regular audits so surprises stay short.

Part V: Launch & Beyond #

1. Pre-Launch Testing #

  • Run manual + automated scripts with real data.
  • Capture Looms of the core flow so you can spot jitter.

2. Launch Readiness Checklist #

  • Confirm smoke suite, observability, and rollback plan.
  • Document go/no-go criteria everyone can read in five minutes.

3. Post-Launch Operating Rhythm #

  • Weekly deploys with smoke + observability reviews.
  • Friday founder update referencing metrics.
  • Monthly complexity audit.

Part VI: Budget Strategy #

Break spending into three buckets:

  • Instrumentation: Monitoring, smoke tests, prompt tooling.
  • Audits: Fractional reviews, Giga diagnostics, external code scans.
  • Targeted fixes: Pay for gnarly migrations, not every ticket.

Part VII: Tool-Hopping with Intent #

  • Assign each AI a role (builder, reviewer, tester).
  • Share context via prompt packs.
  • Keep a shared scratchpad of “what we told the bots today.”

Part VIII: Next.js at Scale #

  • Harden App Router layouts and runtime configs.
  • Decide which routes run on Edge vs. Node and document it.
  • Wrap data fetching in helpers so the bot stops improvising.

Part IX: Long-Term Governance #

  • Maintain prompt version history.
  • Hold quarterly incident postmortems.
  • Update documentation as part of every feature.
  • Review success metrics (activation, retention, support load).

Part X: When to Ask for Help #

Reach out when:

  • Tests stop telling the truth.
  • Deploys fail more than succeed.
  • You can’t map architecture in under 30 minutes.
  • Investors ask for clarity you can’t provide.

Giga offers diagnostics, recovery sprints, and guardrails. Book a session.

Printable Checklist #

  • MVP scope locked (Three-Feature Rule)
  • Documentation started (<2 pages covers architecture + prompts)
  • Observability + smoke tests live
  • Workspace prompts versioned in repo
  • Complexity Wall plan drafted
  • Launch checklist populated
  • Budget allocated for recovery support
  • Post-launch cadence defined

This guide links to every deep dive you’ll need. Bookmark it, share it with your team or advisors, and come back whenever AI feels like it’s outpacing you. With structure, you can build production-grade software—even without a single full-time engineer.

Ready to operationalize the entire playbook? Start with Giga.