Chunking

Chunking is the process of breaking large pieces of data or text into smaller, more manageable segments called “chunks” to enhance processing by AI models, particularly in large language models (LLMs) and semantic retrieval systems.

Key Points:

• Efficiency: Smaller chunks are faster for AI models to process.
• Focus: Smaller chunks help AI focus on specific data, improving accuracy.
• Scalability: Chunking enables handling of large datasets.

Impact on Search Results:

• Large Chunks: May contain multiple ideas, diluting relevance and making it harder for models to retrieve focused results.
• Small Chunks: Provide more focused, precise results, especially for queries related to a single topic.

The right chunk size is crucial for optimizing AI search results—smaller chunks improve precision, while larger ones offer more context but may reduce relevance.