Chunking
Best practices for implementing document chunking for RAG
Last updated
Was this helpful?
⌘Ctrlk
Best practices for implementing document chunking for RAG
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.
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.
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
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
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
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.
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.
Mastering chunking helps:
Improve document comprehension for AI
Boost data extraction accuracy
Deliver better performance across document-based workflows in StackAI
Last updated
Was this helpful?
Was this helpful?