Chunking

Best practices for implementing document chunking for RAG

Chunking: Optimizing Data Retrieval in Stack AI Workflows

Chunking is a key technique in AI-powered document processing. In StackAI, using the right chunking strategy can greatly enhance how effectively machine learning models understand and extract data from documents.

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What is Chunking in StackAI?

Chunking = Breaking large documents into smaller, manageable parts.

• Used in StackAI’s "Files" and "Documents" nodes.

• Ensures input fits within AI model token limits.

• Can be configured via the gear icon in relevant nodes.

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Chunking Methods

1. Naïve Chunking (Fixed-Length)

Splits text by character, word, or token count.

• Pros:

  • Fast and simple to implement

  • Predictable processing time

• Cons:

  • May break sentences or ideas

  • Can reduce AI comprehension

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2. Sentence-Based Chunking

Splits text along natural sentence boundaries.

• Pros:

  • Preserves meaning and structure

  • Enhances AI understanding

• Cons:

  • More computationally intensive

  • Chunk sizes can vary

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Optimizing Chunk Configuration

Chunk Size

• Choose based on your model's capabilities.

• Tradeoff:

  • Larger chunks = better context but risk hitting token limits.

  • Smaller chunks = faster, but may lose coherence.

• Recommended: 200–1,000 tokens

Chunk Overlap

• Adds continuity between chunks.

• Suggested: 15–30% overlap

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Best Practices for Stack AI Users

• Use sentence-based chunking for documents with rich content.

• Tune chunk size to match your AI model's limits.

• Experiment with overlap percentages to preserve context.

• Iteratively test to ensure optimal results.

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Technical Tips

• Configure chunking inside "Files" and "Documents" nodes.

• Continuously monitor model performance as you adjust settings.

• Align your chunking strategy with your specific ML model needs.

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Why It Matters

Mastering chunking helps:

• Improve document comprehension for AI

• Boost data extraction accuracy

• Deliver better performance across document-based workflows in StackAI