Chunking

Chunking splits long documents into smaller, retrievable segments for use in embeddings databases and retrieval-augmented generation pipelines. Chunk size matters enormously — too small and the model loses context, too large and retrieval becomes imprecise and exceeds the model's effective attention. Common strategies include fixed-size chunking (e.g., 512 tokens with 50-token overlap), recursive character splitting (split by paragraphs, then sentences, then characters), semantic chunking (split at topic boundaries detected by embeddings), and document-structure-aware chunking (respect markdown headers, PDF pages, or HTML sections). Libraries like LangChain, LlamaIndex, and Unstructured.io provide chunking utilities. Real-world examples include splitting earnings reports for financial Q&A, chunking legal contracts for clause-level search, and partitioning support documentation for help-desk chatbots. AI governance frameworks document chunking strategy in AI compliance evidence because poor chunking can systematically exclude information from retrieval — affecting accuracy, AI risk management, and responsible AI outcomes.

Centralpoint Handles Chunking, Indexing, and Retrieval as One Pipeline: Oxcyon's platform processes documents into governed chunks on-premise, then exposes them via model-agnostic LLM access — OpenAI, Gemini, Llama, embedded. Centralpoint meters consumption, keeps prompts and skills locally, and embeds chunked-content chatbots into your portals with one JavaScript line.


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