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Conclusion

You started with a blank Next.js project. Now you have a production-ready AI-powered review summarization app deployed to Vercel. Let's recap.

What you built

Section 1: Foundations

  • Modern Next.js 16 app with TypeScript and Tailwind
  • Type-safe data layer with Zod schemas
  • Review display components with star ratings
  • Dynamic routes with static generation
  • Deployed to Vercel with automatic CI/CD

Section 2: AI SDK integration

  • AI Gateway setup with secure API keys
  • First AI summary using generateText
  • Prompt engineering for consistent output
  • Streaming summaries with streamText
  • Structured data extraction with generateObject

Section 3: Production readiness

  • Smart caching for 97% cost reduction
  • Error handling with graceful fallbacks
  • AI Gateway model fallbacks
  • Cost awareness and optimization
  • Observability with structured logging and alerts

The complete architecture

User visits /mower
       ↓
Next.js checks cache
       ↓
[Cache HIT] → Return instantly (50ms)
       ↓
[Cache MISS] → Call AI Gateway
       ↓
AI Gateway → Claude API
       ↓
Generate summary (~2s)
       ↓
Cache result (1 hour)
       ↓
Return to user

Performance:

  • First visit: ~2-4s (AI generation)
  • Cached visits: ~50ms (instant)
  • Cost: ~$0.002 per unique summary
  • With caching: 97% cost reduction

Key patterns you learned

1. Server Components for AI AI calls happen server-side. Users never see your API keys. No client-side JavaScript needed for AI features.

// Server Component - runs on server
export async function AIReviewSummary({ product }) {
  const summary = await summarizeReviews(product); // Server-side AI call
  return <div>{summary}</div>;
}

2. Prompt engineering matters A good prompt is the difference between "meh" and "production-ready."

// Bad: "Summarize these reviews"
// Good: Specific format, examples, constraints, tone guidance

3. Structured output with Zod Don't parse AI text—tell the model exactly what shape you want.

const { object } = await generateObject({
  model: "anthropic/claude-sonnet-4.5",
  schema: ReviewInsightsSchema, // Zod schema
  prompt,
});
// object is typed and validated

4. Cache aggressively AI is expensive. Cache results. Most users see cached content—same UX, fraction of the cost.

export async function summarizeReviews(product: Product) {
  "use cache";
  cacheLife("hours");
  cacheTag(`product-summary-${product.slug}`);
 
  // AI call runs once, result cached for 1 hour
  const { text } = await generateText({ ... });
  return text;
}

5. Fail gracefully AI will fail. Handle it. Show users something useful instead of error screens.

Where to go from here

Your app is complete, but there's always more to explore:

Add more AI features:

  • Product comparisons ("How does X compare to Y?")
  • Review sentiment over time ("Are reviews getting better or worse?")
  • Personalized recommendations ("Based on your history...")

Improve the UI:

  • Loading skeletons during AI generation
  • "Regenerate" button for on-demand summaries
  • Admin dashboard for cache management

Scale considerations:

  • Rate limiting for public APIs
  • Queue-based processing for bulk operations
  • Multi-tenant cost tracking

Advanced AI patterns:

  • Streaming responses for long-form content
  • Multi-model pipelines (cheap model for filtering, expensive for analysis)
  • Fine-tuning for domain-specific language

You did it! I am very proud of you!

Course complete! 🎉