RAG Semantic Chunking & Overlap Visualizer

Inspect text chunking boundaries, token counts, and overlapping sliding-window segments for vector embedding pipelines.

Total Chunks
4
Avg Tokens / Chunk
69
Effective Overlap
50 chars (~13 tokens)
Target Chunk Size:300 characters (~75 tokens)
Chunk Overlap:50 characters (~13 tokens)
Chunk #1293 chars · ~74 tokens

# Retrieval-Augmented Generation (RAG) Architecture Retrieval-Augmented Generation (RAG) is a pattern that optimizes the output of large language models by referencing an authoritative knowledge base outside of its training data before generating a response. ## Why Semantic Chunking Matters

Chunk #2327 chars · ~82 tokens

response. ## Why Semantic Chunking Matters LLMs have fixed context windows and cost budgets. Splitting extensive documents into distinct, semantically coherent chunks enables vector databases (like pgvector, Pinecone, or Qdrant) to retrieve only the top 3 to 5 most relevant paragraphs rather than the entire 50-page document.

Chunk #3298 chars · ~75 tokens

top 3 to 5 most relevant paragraphs rather than the entire 50-page document. ### Chunk Overlap Strategy When documents are split arbitrarily at character boundaries, sentences and critical thoughts get severed in half. By introducing a 10% to 20% sliding window overlap between consecutive chunks,

Chunk #4184 chars · ~46 tokens

liding window overlap between consecutive chunks, the trailing context of chunk N is prepended to chunk N+1, preventing broken sentence fragments and maintaining query recall accuracy.

The Impact of Chunk Size & Overlap on RAG Accuracy

In Retrieval-Augmented Generation (RAG), semantic chunking is the single most important factor determining vector search recall. If chunks are too small (e.g. 50 tokens), embeddings lose semantic context and meaning. If chunks are too large (e.g. 2,000 tokens), specific facts get diluted across unrelated paragraphs, causing retrieval ranking algorithms to miss the target information.

Why Sliding Window Overlap is Critical

Without overlap, a query matching a concept spanning the boundary between two adjacent chunks will fail because neither chunk contains the complete thought. Setting a 10% to 20% chunk overlap ensures continuous semantic continuity across chunk boundaries.

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