RAG Chunking Visualizer

Retrieval-augmented generation (RAG) gives a language model access to information selected at request time. A retrieval pipeline usually prepares documents, splits them into chunks, creates embeddings, and stores those vectors with source metadata. At query time, the system embeds the question, retrieves relevant chunks, and places the strongest evidence into the model context. Chunk boundaries matter: chunks that are too small lose meaning, while chunks that are too large can dilute relevance and waste context.

At query time, the system embeds the question, retrieves relevant chunks, and places the strongest evidence into the model context. Chunk boundaries matter: chunks that are too small lose meaning, while chunks that are too large can dilute relevance and waste context. Overlap can preserve sentences or ideas that cross a boundary. It should be large enough to retain continuity but small enough to avoid flooding retrieval results with near-duplicates. Production systems should evaluate chunk size, overlap, retrieval quality, answer completeness, latency, and cost on representative questions.