RAG Chunking Visualizer

Visualize how your documents will be split for Retrieval Augmented Generation

Chunking Settings

Chunk Size (tokens)

512

Overlap (tokens)

Split Strategy

Source Document

268 tokens

Artificial Intelligence (AI) has revolutionized the way we interact with technology. Machine learning, a subset of AI, enables computers to learn from data without being explicitly programmed. Deep learning, which uses neural networks with multiple layers, has achieved remarkable success in image recognition, natural language processing, and game playing.

Large Language Models (LLMs) like GPT-4 and Claude represent a significant breakthrough in AI. These models are trained on vast amounts of text data and can generate human-like responses, answer questions, and even write code. The transformer architecture, introduced in 2017, has been fundamental to their success.

Retrieval Augmented Generation (RAG) is a technique that combines the power of LLMs with external knowledge bases. By retrieving relevant information from a database and providing it as context to the model, RAG systems can produce more accurate and up-to-date responses. This approach is particularly useful for domain-specific applications where the model needs access to specialized knowledge.

Chunks Preview

0 chunks generated

Chunking Statistics

Total Chunks

Total Tokens

Avg Tokens/Chunk

Overlap Percentage

10%

Chunking Tips:

Use 512-1024 tokens for most RAG systems
Add 10-20% overlap to preserve context
Sentence splitting works well for Q&A
Paragraph splitting for structured docs