Sep 16, 2026

Stop Chunking Like It's 2022 — Yuval Belfer, AI21 Labs

Yuval Belfer addresses the fundamental challenge in Retrieval Augmented Generation (RAG): the optimal chunk size is query-dependent, but chunking decisions are fixed during indexing. This "information problem" leads to significant recall loss, as shown by experiments demonstrating a 20-40% gap between fixed chunking and an oracle. His proposed solution, multiscale indexing, involves creating multiple document copies with different chunk sizes, querying all of them, retrieving whole documents, and merging results using Reciprocal Rank Fusion, yielding substantial recall improvements with minimal latency increase.

Jul 07, 2026

How we taught agents to use good retrieval - Hanna Lichtenberg, Mixedbread AI

Mixedbread AI addresses the "Oracle Gap" – the disparity between LLM reasoning and retrieval capabilities – by developing agents trained to use advanced search tools effectively. They demonstrate how current LLMs generate poor queries due to training biases and introduce a sophisticated agent harness with diverse search tools and a unique training regimen, including supervised fine-tuning and reinforcement learning with custom rewards, to teach agents to form precise semantic queries. This approach significantly improves performance on benchmarks like Oblique Congress and Snowflake's Match QA, closing the gap between theoretical perfect retrieval and real-world agent performance.

Aug 25, 2025

Conext Engineering for Engineers

Jeff Huber of Chroma argues that building reliable AI systems hinges on 'Context Engineering'—the deliberate curation of information within the context window. He challenges the efficacy of long-context models, presenting a 'Gather and Glean' framework to maximize recall and precision, and discusses specific challenges and techniques for AI agents, such as intelligent compaction.

Aug 06, 2025

How to look at your data — Jeff Huber (Choma) + Jason Liu (567)

A detailed summary of a talk by Jeff Huber (Chroma) and Jason Liu on systematically improving AI applications. The talk covers using fast, inexpensive evaluations for retrieval systems (inputs) and applying structured data analysis and clustering to conversational logs (outputs) to derive actionable product insights.

Jul 29, 2025

Scaling Enterprise-Grade RAG: Lessons from Legal Frontier - Calvin Qi (Harvey), Chang She (Lance)

A summary of the talk by Harvey and LanceDB on building a highly optimized retrieval architecture for the legal profession. It covers challenges like query complexity and data scale, the importance of evaluation, and how LanceDB's multimodal lakehouse architecture provides the necessary foundation.