What is Chunking in AI & RAG?

Chunking is the process of breaking down large pieces of text (like PDFs, documentation, or codebases) into smaller, semantically coherent passages before converting them into vector embeddings for a Retrieval-Augmented Generation (RAG) system.

Why is Chunking Important?

1. Embedding Model Limits: Embedding models have fixed token limits (e.g., 512 or 8,192 tokens). You cannot create an accurate embedding vector for an entire 300-page book at once.
2. Semantic Precision: If a chunk is too long, its vector embedding becomes diluted. Keeping chunks concise and focused ensures high retrieval accuracy for specific user queries.
3. Cost & Latency Optimization: Precise chunking allows sending only the most relevant text snippets into the LLM context, saving tokens and reducing response latency.

Common Chunking Strategies

• Fixed-size Chunking: Splitting text into fixed token or character lengths (e.g., 500 tokens) with a sliding overlap (e.g., 50 tokens) to maintain context across chunk boundaries.
• Recursive Character Chunking: Splitting recursively using natural delimiters (\n\n, \n, . , ) to preserve sentence and paragraph integrity.
• Document-Structure Chunking: Splitting according to headers, Markdown sections, HTML tags, or code functions.
• Semantic Chunking: Grouping sentences dynamically based on their semantic embedding distance.

When to Use Chunking?

✅ Building document Q&A and RAG applications.
✅ Indexing long manuals, reports, and knowledge bases into vector databases.
✅ Improving search relevance and reducing LLM hallucinations.