Semantic search

Before Building AI Agents Watch This (Deep Agent Expertise)

Before Building AI Agents Watch This (Deep Agent Expertise)

Nishikant Dhanuka from Prosus Group shares practical lessons on building effective AI agents for e-commerce and productivity. He covers why context engineering is more crucial than prompt tweaking, how to build a modern search pipeline, the failures of pure-chat interfaces, and why a robust evaluation framework is the real competitive advantage.

No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

OpenEvidence founder Daniel Nadler explains how his company solved the semantic search problem in medicine, achieving 40% adoption among US doctors in 18 months. He discusses the strategy of treating physicians as consumers, the future of medical education in the age of AI, and his unique philosophy on motivation and recruiting.

No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

Daniel Nadler, founder of OpenEvidence, discusses the platform's rapid adoption by 40% of US physicians. He explains how OpenEvidence solves the semantic search problem in high-stakes clinical decision-making, its growth strategy of treating doctors as consumers, and how AI will reshape medical education and the role of the physician.

Arvind Jain on building Glean and the future of enterprise AI

Arvind Jain on building Glean and the future of enterprise AI

Arvind Jain, CEO of Glean, details the company's journey from a pre-LLM enterprise search innovator to a leading AI agent platform. He covers their hybrid model strategy, the critical role of permission-aware RAG for security, and how AI agents are creating 'evergreen' documentation and reshaping enterprise workflows.

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.

Unlocking Unstructured Data with LLMs

Unlocking Unstructured Data with LLMs

Shreya Shankar of UC Berkeley discusses DocETL, a MapReduce-style framework that leverages LLMs to extract, analyze, and structure insights from unstructured enterprise data. The conversation covers practical architecture patterns, the role of non-determinism, strategies for model selection (including fine-tuning and multi-LLM pipelines), and the importance of user experience in this emerging field.