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Owen King
Owen King

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Stop Splitting by 500 Tokens: Why Heading-Aligned Chunking Wins RAG

#llm #ai #rag #api

Most RAG pipelines fail before retrieval even starts—because arbitrary token splitters blindly slice sentences, code blocks, and context in half.

When you split text strictly every 500 or 1,000 tokens, boundaries land anywhere: midway through an explanation, inside an HTML table, or cleanly separating an H3 subhead from the critical steps listed below it. Your vector search ends up indexing orphan fragments. The result? Your LLM hallucinates, not because it lacks reasoning, but because its retrieved context was severed from its source meaning.

Humans structure knowledge hierarchically. Headings define logical scopes, prerequisites, and intent.

Heading-aligned chunking honors this structure. By isolating content strictly along logical sections (#, ##, ###), every chunk represents a complete, cohesive thought. You eliminate boundary bleed, retain table integrity, and give your embedding models dense, unambiguous semantic targets to index.

RagScrape makes this painless. Instead of assembling brittle web scrapers, HTML cleaners, and custom chunking scripts, you can turn any public URL into heading-aligned, token-slimmed Markdown in a single API call:

curl -X POST https://rag-scrape-api.owerryking.workers.dev/scrape \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://docs.stripe.com/api",
    "chunk": true,
    "stripLinks": true,
    "embed": true
  }'
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With chunk: true, you get cleanly partitioned sections mapped to the author's original structure. Add embed: true, and RagScrape returns native 384-dimensional embeddings for each chunk alongside token-optimized text—omitting noisy links and images with zero extra infrastructure on your end.

Better chunks mean better embeddings, higher-precision retrieval, and fewer wasted tokens in your prompt window.

Try it free (no signup): https://rag-scrape-api.owerryking.workers.dev/convert — free API key: 50 requests/month. Full docs: https://rag-scrape-api.owerryking.workers.dev/llms-full.txt

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