---
title: "Stop Chunking Like It's 2022"
category: "talks"
date: "2026-06-29"
time: "3:20pm-3:40pm"
track: "Search & Retrieval"
room: "Track 3"
speakers: ["Yuval Belfer", "Niv Granot"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Search & Retrieval"
scheduleRoom: "Track 3"
scheduleLabels: ["Search & Retrieval", "Track 3", "session", "confirmed"]
---
# Stop Chunking Like It's 2022

## Conference Context
- Date/time: 2026-06-29 · 3:20pm-3:40pm
- Track/room: Search & Retrieval · Track 3
- Speaker(s): Yuval Belfer, Niv Granot
- Session type/status: session · confirmed

- Track: Search & Retrieval
- Room: Track 3
- Session type: session
- Status: confirmed

## Session 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.

## Media Evidence
[RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot](https://www.youtube.com/watch?v=Ywl4LsvHKzU) (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

- Source video: `youtube-Ywl4LsvHKzU`
- Slide deck: [[youtube-Ywl4LsvHKzU-dense-slides|Dense Slides: RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot]] — 7 visible slide image(s); 7 HTML recreation(s).
![[assets/dense-slides/Ywl4LsvHKzU/slide-001.jpg]]
![[assets/dense-slides/Ywl4LsvHKzU/slide-002.jpg]]
![[assets/dense-slides/Ywl4LsvHKzU/slide-003.jpg]]
- Additional slide evidence: [[youtube-Ywl4LsvHKzU-slides|Slides: RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot]], [[youtube-Ywl4LsvHKzU-reconstructed-slides|Reconstructed Slides: RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot]]
- Slide-derived themes for `youtube-Ywl4LsvHKzU`: world, local, questions, name, answers, assuming, answer, lies.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
- `youtube-Ywl4LsvHKzU` — 8 slide-derived text signals
- Slide-derived themes for `youtube-Ywl4LsvHKzU`: world, local, questions, name, answers, assuming, answer, lies.
- Evidence links for `youtube-Ywl4LsvHKzU`: [[youtube-Ywl4LsvHKzU]], [[youtube-Ywl4LsvHKzU-slides]], [[youtube-Ywl4LsvHKzU-dense-slides]], [[youtube-Ywl4LsvHKzU-reconstructed-slides]]

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
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.

## People
- [[yuval-belfer]]
- [[niv-granot]]

## Supporting Slides
- [[youtube-Ywl4LsvHKzU-slides]] — extracted from the related public AI Engineer video.

## Slide Evidence
- Slide-only cropped deck: [[youtube-Ywl4LsvHKzU-dense-slides]] (7 viable slide images).
- Related slide/OCR pages:
- [[youtube-Ywl4LsvHKzU-dense-slides]]
- [[youtube-Ywl4LsvHKzU-reconstructed-slides]]
- [[youtube-Ywl4LsvHKzU-slides]]
- Slide-derived terms: `world`, `fifa`, `local`, `name`, `questions`, `same`, `future`, `wife`, `first`, `mother`, `schema`, `prob`, `answers`, `assuming`, `answer`, `lies`, `certain`, `chunk`

## Synthesis
### Synthesized Breakdown
# Stop Chunking Like It's 2022 ## Conference Context - Date/time: 2026-06-29 · 3:20pm-3:40pm - Track/room: Search & Retrieval · Track 3 - Speaker(s): Yuval Belfer, Niv Granot - Session type/status: session · confirmed - Track: Search & Retrieval - Room: Track 3 - Session type: session - Status: confirmed ## Session Description Every RAG system bets everything on a single chunk size. 500 tokens? 800? Pick wrong, and half your queries fail before they start.

### Speaker And Company Context
- [[yuval-belfer|Yuval Belfer]] — Sr. Developer Advocate at [[ai21|AI21]].
- [[niv-granot|Niv Granot]] — Tech Group Lead at [[ai21-labs|AI21 Labs]].

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material
- [[youtube-Ywl4LsvHKzU]] — related YouTube source page.
- [[youtube-Ywl4LsvHKzU-slides]] — slide evidence.
- [[youtube-Ywl4LsvHKzU-reconstructed-slides]] — slide evidence.
- [[youtube-Ywl4LsvHKzU-dense-slides]] — slide evidence.

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.