Comparison
Chunking vs Semantic Chunking
Chunking and Semantic Chunking are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Chunking
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning.
A 50-page PDF split into 200-token chunks with 50-token overlap → ~150 chunks indexed.
When you would reach for Semantic Chunking
When documents have variable topic density and recursive chunking is producing low-quality retrievals.
A meeting transcript: semantic chunker breaks on topic-change moments rather than arbitrary token windows.
Frequently asked
What is the difference between Chunking and Semantic Chunking?
Chunking: Chunking is the process of splitting source documents into smaller passages before embedding them for retrieval. Chunk size and boundaries control how relevant retrievals will be. Semantic Chunking: Semantic chunking embeds each sentence and inserts a chunk boundary wherever consecutive embeddings diverge sharply — producing chunks that respect topic boundaries rather than character counts.
When should I use Chunking vs Semantic Chunking?
Always — chunking is upstream of every other RAG decision. Spending 2 hours on chunking strategy commonly beats 2 weeks of prompt tuning. When documents have variable topic density and recursive chunking is producing low-quality retrievals.
Are Chunking and Semantic Chunking the same thing?
No. Chunking is agents & tools; Semantic Chunking is agents & tools. They are related but address different parts of the AI stack.