chunking

RAG tutorials rarely cover a key issue: chunk boundaries are almost always wrong; when documents are chunked and embedded, assumptions are actually being made about which context belongs together, and these assumptions often do not hold.

If a RAG pipeline returns results that are technically correct but contextually useless, the problem is almost certainly in the chunking or metadata, not the embedding model.

Before calling a time-series model, you should first estimate the number of tokens the sequence consumes; if the estimate exceeds the model's context window, you must downsample or chunk, and this step cannot be skipped.

Chunking causes loss of document-level coherence, manifesting as inconsistent terminology translation and incorrect pronoun references, which constitutes an error in legal, medical, or technical content.

Even when a large model's context window is large enough, production pipelines still deliberately chunk documents, for reasons including per-inference cost growing roughly linearly with token count, unacceptable latency on very long requests, and output length limits.

In RAG, small chunks are more likely to hit the most specific facts but may lose context, while large chunks retain more context but tend to introduce irrelevant information, so chunk granularity requires a trade-off.

Chunking is the process of cutting raw documents into the smallest retrievable units; it is one of the earliest and most critical decisions in a RAG pipeline, and its quality affects the effectiveness of downstream embedding, retrieval and generation.

Chunking refers to splitting long documents into text units suitable for indexing and fitting into the context window; common approaches include fixed token windows with overlap, splitting by paragraph or heading, and dynamic merging by semantic similarity.

Chunk boundaries should reflect the structure of the source material; for example, policies need to preserve conditions, exceptions and section headings, while contracts may need clause boundaries and cross-references.

The goal of an efficient chunking strategy is to create units that are focused enough to be retrieved and complete enough to be interpreted.

When a RAG pipeline returns results that are 'technically correct but contextually useless', the problem is almost certainly in the chunking or the metadata, not the embedding model.

Chunking is a key step in the RAG pipeline; common splitting methods include splitting by fixed length, by paragraph boundaries, and by semantic similarity, and chunk size requires trade-offs.

The choice of chunking granularity is essentially a trade-off between precision and contextual completeness: chunks that are too small allow precise location of code but lose context, while chunks that are too large retain context but degrade retrieval accuracy and vector representation quality.