LLM-First Web Design: Turning Pages into Knowledge APIs

“What if every webpage treated ChatGPT like its #1 reader?” A thought-experiment—plus a build plan—for re-engineering the entire stack around large-language-model consumption.

1. Treat Content Like Code

The canonical asset isn’t HTML—it’s a signed JSON-LD blob that stores every claim, citation, and hash. Writers “commit” edits the way developers push to Git. Humans see a rendered skin; machines fetch crystal-clear structure.

2. Semantic Chunking & Role Tags

Wrap each sentence in a micro-span (<span data-claim="C123">) and label its function—claim, evidence, counter-argument. A manifest file lets an LLM reconstruct arguments without rhetorical guesswork.

3. Ship Your Own Embeddings

At build-time, generate OpenAI-compatible vectors for every chunk and host them under /embeddings/{id}.bin. Expose /.well-known/nearest so agents can hit an HNSW index directly—no paid re-embedding, near-zero latency.

4. Time-Travel URLs & Reproducible Answers

Serve a Memento-Datetime header and pin-able hashes (/snapshot/2025-05-01T17:00Z). LLM citations stay valid forever; auditors can diff any two versions byte-for-byte.

5. One-Hop Discovery Endpoints

• /robots.txt advertises a crawl budget just for LLMs.
• /.well-known/llm-feed?topic=⇢ returns the 50 most salient claims for a query.
• Optional mini-SPARQL endpoint lets power users run live graph queries.

6. Prompt-Injection Hardening

Default machine endpoint is plain JSON; the human HTML lives in a <script type="text/plain+human"> wrapper. Crawlers must opt in, slashing the risk of stray user-supplied prompt fragments.

7. Moon-Shot Extras

• Self-expiring HTML that nudges agents toward the structured layer.
• Publish authors’ cot.json (chain-of-thought) so models can learn expert reasoning paths.
• WebAssembly summarizer shipped with every page—humans & bots get pixel-perfect identical abstracts.

8. Snapshot of Pros & Cons

• Pros—Zero parsing ambiguity, cheaper RAG pipelines, transparent edit history.
• Cons—Authoring feels like coding, bigger headers, new privacy surface (vectors can leak identity).

9. Starter Tech Stack

• Static generator (Eleventy) extended to spit out JSON-LD + vectors.
• Vector DB (Weaviate) behind the /nearest endpoint.
• Edge workers (Cloudflare) inject version headers & snapshot routing.

10. Why Bother?

Because it turns “content” into an addressable truth graph. LLMs stop hallucinating and start retrieving—exactly the future we keep asking for.