Data Engineering
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Definition
Techniques for splitting large documents into smaller segments for storage in vector databases and retrieval in RAG systems. Strategies include fixed-size, sentence-based, or semantic chunking.
Why it matters
Bad chunking breaks context, leading to AI hallucinations; good chunking enables accurate answers.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
Data Pipeline
The automated plumbing that moves data from where it's collected to where it's analyzed and used.
From vocabulary to outcomes
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