Fixed-size Chunking
Also known as: Token-based chunking, Fixed-window chunking
Slice documents every N tokens with a small overlap. Fast and format-agnostic, but cuts through headings and mid-topic.
The simplest chunking strategy in RAG. Documents are split at fixed token boundaries (often 256 to 1024 tokens) with a small overlap so that context spanning a boundary is not fully lost. Runs at scale on any format but ignores meaning and structure.
In practice
The baseline every RAG interview compares against. Understanding why it degrades on structured docs is the setup for semantic and recursive chunking.
How it compares
Fixed-size cuts every N tokens regardless of meaning; semantic chunks at topic boundaries by finding embedding-similarity drops.
Fixed-size ignores the document; recursive splits by the document's own hierarchy of sections, paragraphs, and sentences.
Comparisons that include Fixed-size Chunking
Related topics
Questions that mention this term
Related terms
AI System Design
End-to-end design of production LLM systems: ingestion, retrieval, serving, eval, monitoring.
BM25
Classical TF-IDF-style lexical scoring: a surprisingly strong baseline, still the first stage of many hybrid RAG pipelines.
Chunking
Cutting documents into retrievable pieces before embedding them.
Context Engineering
Deciding what makes it into the model's context window (prompt + RAG + history + memory), and why.
Context Rot
Long-running chats degrade: early instructions get forgotten, and tool calls become less reliable.
Context Window
The max number of tokens a model can attend to at once.