Fixed-Size Chunking

Fixed-size chunking is the simplest segmentation approach, dividing text into uniform segments based on character count or token length. This straightforward method offers implementation simplicity but can break logical units of information:

Core Mechanism: Text is divided into chunks of predetermined size (typically 256-1024 tokens), regardless of content structure. When a chunk reaches the maximum size, the segmentation creates a new chunk and continues the process.

Advantages:

• Simplicity: Extremely easy to implement with minimal computational overhead
• Predictable Memory Usage: Consistent chunk sizes enable reliable resource allocation
• Uniform Processing: Standardized segment lengths simplify downstream handling

Disadvantages:

• Semantic Disruption: Often cuts through sentences, paragraphs, and conceptual units
• Context Loss: Related information may be arbitrarily split across different chunks
• Retrieval Inefficiency: Can lead to irrelevant sections being included in chunks

Implementation Example:

Best Use Cases: Fixed-size chunking works adequately for homogeneous content with uniform structure, initial prototyping, and when processing speed is prioritized over retrieval quality. It's often the starting point for RAG systems before more sophisticated approaches are implemented.