Stop Chunking Like It's 2022

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Official Description

Every RAG system bets everything on a single chunk size. 500 tokens? 800? Pick wrong, and half your

queries fail before they start. But here's what nobody tells you: all the picks are wrong; there is

no single chunk size that works for all queries. We ran oracle experiments across meeting

transcripts, story chapters, and TV scripts. The result? Queries disagree violently on what chunk

size works best - sometimes by 40 percentage points. Your "tuned" chunk size isn't a compromise;

it's systematic underperformance. In this talk, we'll expose why fixed chunking fails and show you a

dead-simple fix: index at multiple chunk sizes, aggregate at retrieval time using Reciprocal Rank

Fusion. No retraining. No LLM overhead. Just 1-37% better recall across benchmarks by letting

queries vote with their ranks instead of forcing them into one-size-fits-all boxes. Walk away

knowing exactly when your chunk size is sabotaging you - and how to stop leaving 20-40% of your

retrieval performance on the table.

Related YouTube Video

RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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