LEVEL 02 · LESSON 14
Semantic chunking
Semantic chunking tries to split where the meaning changes.
What it is
Semantic chunking chooses boundaries using changes in meaning or topic, rather than only a fixed character or token count.
Why it matters
It can keep related reasoning together when headings are weak or documents switch subjects inside long sections.
A business example
A meeting transcript moves from delivery delays to pricing. A semantic splitter separates the discussion at the topic change, helping later searches avoid mixing two different decisions.
How it actually works
One approach compares embeddings of adjacent sentences or groups of sentences. A large change in similarity suggests a boundary. Other approaches use language models or document structure. Set minimum and maximum sizes so the result remains practical. Evaluate against a simpler structure-based baseline: topic changes are imperfect signals, and extra processing must improve retrieval enough to justify its cost.
A common misconception
Semantic chunking is not automatically superior. Lists, tables, and legal-style exceptions can be damaged by a splitter that lacks document structure.
ONE SENTENCE TO REMEMBER
Semantic chunking tries to split where the meaning changes.
CHECK YOUR UNDERSTANDING
A team plans to switch from heading-based chunking to semantic chunking for its product manuals. What is the sensible approach?
Show the answer
Answer: C.
Semantic chunking tries to split where meaning changes, but topic signals are imperfect and splitters without document structure can damage lists, tables, and exceptions. Evaluate against a simpler baseline and keep size limits so the extra processing earns its cost.
Take it into your next meeting
Would topic-aware boundaries help your documents more than their existing headings?
One more concept, understood.
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