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Posted on • Originally published at aitechconnect.in

How to Optimise RAG Retrieval: A 2026 Chunking Playbook

Originally published on AI Tech Connect.

What builders shipping RAG need to know Retrieval, not generation, is the failure point — when a RAG answer is wrong, the relevant passage was missing or buried roughly 73% of the time. Chunking is the highest-ROI lever — it costs nothing extra at query time and decides what the retriever can even find. Start with recursive character splitting at 512 tokens with 50-100 tokens of overlap — the benchmark-validated default for 2026. Reach for semantic chunking selectively — it helps documents with abrupt topic shifts and earns its cost only when structure is uneven. Retrieval-augmented generation has become the default pattern for grounding a language model in your own data — whether that is a corpus of Indian case law, a UK fintech's internal knowledge base, product documentation, or…


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