Ditch Naive Chunking: Late Chunking RAG in Spring AI

Naive text chunking breaks your RAG pipeline by slicing context at arbitrary token boundaries before your embedding model ever sees the text. Late chunking fixes this structural flaw by running a long-context transformer over the full document first, then pooling chunk vectors directly from fully contextualized token states.

Why Most Developers Get This Wrong

Splitting documents into isolated 512-token slices with Spring AI's TokenTextSplitter prior to embedding, completely destroying document-level semantics and cross-paragraph references.

Adding heavy LLM contextual-summarization calls or bloated chunk overlaps to salvage lost context, inflating API costs and query latency for zero structural gain.

Ditch Naive Chunking: Late Chunking RAG in Spring AI

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