Break documents into optimized units for accurate AI retrieval and reduced hallucinations
Chunking is the process of breaking large documents, knowledge bases, emails, tickets, SOPs, logs, and other content into smaller units that AI models can understand and retrieve effectively. These smaller units, called chunks, are converted into embeddings and stored in vector databases for fast and accurate retrieval.
Chunking is one of the most influential parts of any RAG or enterprise AI system. Done well, it dramatically improves accuracy. Done poorly, it leads to hallucinations, missing context, or irrelevant answers.
As enterprises adopt RAG and AI assistants, the quality of chunking often determines the quality of the entire system.
Well structured chunks return relevant information without noise.
Clear and complete chunks help models stay grounded in factual context.
Smaller, well indexed chunks make retrieval efficient at scale.
Models produce more consistent, context aware answers when chunks are designed properly.
Ideal for industries with regulatory responsibilities: financial services • healthcare • retail • technology
Chunking ensures that generative AI references the most relevant information every time.
All chunking strategies follow three steps.
Chunk size affects context, completeness, and retrieval quality.
Split by paragraphs, sections, sentences, headings, or token limits.
Metadata improves filtering, relevance, governance, and accuracy.
Enterprises often need different chunking strategies for different content types.
Different content requires different chunking logic and metadata enrichment.
Key metadata to include: document title, section or heading, date and version, author or owner, entity or department, category or workflow, tags extracted from content, and access permissions.
Chunking requires domain knowledge, experimentation, and strong engineering discipline. Gyde provides the people, platform, and process to build effective chunking pipelines.
A team focused entirely on your chunking implementation.
Everything you need to build production-grade chunking pipelines.
Your chunking strategy is designed and productionized through a structured blueprint.
Chunking becomes a long term foundation for your enterprise AI strategy.
It depends. Most enterprise systems use 150 to 500 tokens with overlap.
No. Policies, logs, manuals, and tickets each need different approaches.
Yes. Poor chunking increases hallucination rates.
Yes. Gyde automates chunking with rule based and semantic methods.
Yes. Gyde versions all chunks for audit and compliance.
Start your AI transformation with production ready chunking strategies delivered by Gyde.
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