🔍 RAG Chunk Size Optimizer

📄 Document & Model

Paste sample text or enter average document length (tokens) The quick brown fox jumps over the lazy dog. RAG systems rely on chunking to retrieve relevant passages. Choosing the right chunk size improves accuracy and reduces cost. For Q&A, smaller chunks (256-512) work best. Summarization benefits from larger chunks (1000-2000). Code analysis uses medium chunks (500-1000). Overlap helps maintain context. Embedding model text-embedding-3-small (1536d) — $0.02/1K tokens text-embedding-3-large (3072d) — $0.13/1K tokens ada-002 (1536d) — $0.10/1K tokens cohere-embed-v3 (1024d) — $0.10/1K tokens Chunk size (tokens): 512 Overlap (%): 20%

📊 Estimated Outputs