RAG Chunking & Overlap Visualizer
Visualize how fixed-window vs. semantic paragraph splitting divides your documents into chunks, token budgets, boundary overlaps, and pgvector storage.
Why 80% of RAG Hallucinations Start at Ingestion & Chunking
Teams often blame their foundation model when an enterprise RAG copilot hallucinates—yet in almost every architecture audit we run, the root cause is naive fixed-character chunking that slices tables, JSON blocks, or legal clauses in half.
This visualizer lets your engineering team inspect exact chunk boundaries, compare Markdown/paragraph-aware splitting against naive character windows, and estimate 1536-dimensional pgvector index storage before running an ingestion job.
How to Tune Chunk Size & Overlap
Test real excerpts from your PDFs, Markdown docs, or support articles:
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Frequently Asked Questions
What is the optimal chunk size for enterprise RAG?
For technical documentation, policies, and B2B support tickets, 200 to 450 tokens per chunk with 10%–20% boundary overlap and prepended section headings typically maximizes recall when paired with hybrid BM25 + vector retrieval and cross-encoder reranking.
Why combine pgvector with BM25 lexical search?
Dense embeddings capture conceptual similarity, while lexical BM25 matches exact SKUs, invoice IDs, error codes, and proper nouns. Combining both with Reciprocal Rank Fusion (RRF) inside PostgreSQL eliminates brittle multi-database sync pipelines.
