Next.js LLM SEO Handbook: The Developer's Guide to AI Discoverability

Make your Next.js site discoverable by AI models and search engines

This handbook shows you how to make your Next.js App Router site discoverable by AI models like ChatGPT, Claude, and Perplexity, while improving traditional SEO.

Who am I?

I'm Emeruche Ikenna, a senior frontend developer who has implemented LLM SEO strategies across multiple Next.js projects. This handbook reflects tested techniques and best practices for making your content discoverable by AI models.

What you'll learn:

• How LLMs discover and retrieve your content
• Dynamic sitemap generation for blogs and e-commerce
• Implementation of llms.txt for AI crawlers
• JSON-LD structured data (BlogPosting, Product, Person, Organization, and more)
• Rendering strategies (SSG, SSR, ISR) optimized for AI crawlers
• Internal linking patterns for LLM retrieval
• Performance optimization for faster crawling
• robots.txt configuration for AI bots
• Canonical URLs and Open Graph metadata
• Internationalization (i18n) for multi-language SEO
• Testing and validation tools
• Monitoring and analytics setup

What's inside:

✅ 16 sections with implementation-ready code

✅ TypeScript examples for Next.js App Router

✅ Real-world patterns you can copy and adapt

✅ Advanced JSON-LD schemas (Product, FAQ, HowTo, BreadcrumbList)

✅ Complete metadata API examples

✅ Performance optimization strategies

✅ Testing and validation workflows

✅ Monitoring and analytics setup guides

Perfect for:

• Next.js developers building blogs, documentation sites, or e-commerce

• Developers wanting to optimize for AI discoverability

• Teams implementing SEO best practices

• Anyone looking for a practical, code-first SEO guide

What makes this different:

Most SEO guides focus on traditional search. This handbook covers both traditional SEO and LLM discoverability, with working Next.js App Router code you can use immediately.