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Layering every technique in RAG, one query at a time - David Karam, Pi Labs (fmr. Google Search)

Layering every technique in RAG, one query at a time - David Karam, Pi Labs (fmr. Google Search)

David Karam, formerly of Google Search, presents a pragmatic framework for enhancing RAG systems, advocating a "quality engineering" approach. The talk progresses through a ladder of techniques, from in-memory retrieval and BM25 to custom embeddings, re-ranking, and advanced orchestration, emphasizing that the choice of technique should be driven by empirical analysis of system failures ("loss analysis") and balanced by a "complexity-adjusted impact" mindset.

Scaling and the Road to Human-Level AI | Anthropic Co-founder Jared Kaplan

Scaling and the Road to Human-Level AI | Anthropic Co-founder Jared Kaplan

Jared Kaplan, co-founder of Anthropic, explains how the discovery of predictable, physics-like scaling laws in AI training provides a clear roadmap for progress. He details the two main phases of model training (pre-training and RL), discusses how scaling compute predictably unlocks longer-horizon task capabilities, and outlines the remaining challenges—memory, nuanced oversight, and organizational knowledge—on the path to human-level AI.

Privacy-First Research with OpenSAFELY • Eli Holderness & Hannes Lowette

Privacy-First Research with OpenSAFELY • Eli Holderness & Hannes Lowette

OpenSAFELY offers a new paradigm for medical research by bringing analysis code to sensitive patient data, rather than exporting the data itself. This interview explores its secure, privacy-preserving platform, its custom DSL `ehrql`, and how this model enforces reproducible, hypothesis-driven science while navigating the complexities of real-world clinical data.

Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai

Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai

Will Bryk, CEO of Exa, explains why traditional keyword-based search is insufficient for AI agents and introduces a new paradigm of neural, semantic search. He demonstrates how a hybrid approach, combining neural for discovery and keyword for precision, enables AI agents to perform complex, multi-step information retrieval tasks that were previously impossible.

909: Causal AI — with Dr. Robert Usazuwa Ness

909: Causal AI — with Dr. Robert Usazuwa Ness

Researcher Robert Ness discusses the practical implementation of Causal AI, distinguishing it from correlation-based machine learning. He covers the essential role of assumptions about the data-generating process, key Python libraries like DoWhy and Pyro, the intersection with LLMs, and a step-by-step workflow for tackling causal problems.

[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search)

[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search)

This workshop, led by former Google product directors, introduces a methodology for building reliable and tunable evaluation metrics for LLM applications. It details how to create granular 'scoring systems' that break down complex evaluations into simple, objective signals, and then use these systems for model comparison, prompt optimization, and online reinforcement learning.

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