Content Chunker for LLMs
Split long content into retrieval-friendly chunks with headings, anchors, token estimates
Content
What this tool does
Splits a long article or documentation page into 150-300 word self-contained chunks with clear headings, URL anchors (kebab-case), and approximate token counts - optimized for LLM retrieval, embeddings, and Retrieval-Augmented Generation (RAG) pipelines.
Who it's for: Developers building RAG systems, knowledge bases, or embedding-based search; content strategists preparing text for vector databases.
When to use
- •Prepare documentation for a custom ChatGPT knowledge base.
- •Ingest articles into a vector database with consistent chunk sizes.
- •Build a semantic search index over blog content.
- •Create LLM-friendly support articles with stable anchor links.
How it works
- 1Paste 200+ characters of long-form content.
- 2AI splits the text at topical boundaries into self-contained chunks.
- 3Each chunk gets a heading, a kebab-case anchor, and a token estimate.
- 4Copy individual chunks or use the table of contents as navigation.
Tips
- •Token estimates assume ~4 chars per token; actual count may vary by tokenizer.
- •Publish each chunk with its anchor so LLMs can link to a specific section.
- •Avoid chunks that reference earlier chunks - each should stand alone.