Hybrid Search

Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI

Aug 17, 2026 · 1:03:26

Louis-François Bouchard, Omar Solano, and Samridhi Vaid of Towards AI test context engineering on their AI tutor: keeping full history beat every compaction technique on recall, cost, and latency because 97% of tokens were served from cache (up to 50x cheaper), making summarization a trap unless it shrinks context by more than 50x. Full history recovered specific details 95% vs 32% after summarizing, and distinctive facts survived 800k tokens. Local hardware changes it: a 32k window can't keep everything, and larger models don't widen context. Dense retrieval hit 0% recall at 400k tokens where BM25 got 100%, so they use hybrid retrieval. Rule: name the constraint before compacting.

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

Jul 29, 2025 · 18:42

Will Bryk, CEO of Exa.ai, argues that traditional keyword search engines like Google are ill-suited for AI agents, which need semantic understanding, complex multi-paragraph queries, and comprehensive results. He explains Exa's neural search approach using embeddings to capture ideas and context, unlike keyword-based systems that miss nuances like negation. Bryk demonstrates a live-coded agent combining neural searches (e.g., finding personal sites of SF engineers who like information retrieval) with keyword searches (e.g., retrieving specific GitHub profiles), showing how Exa's API exposes toggles for date ranges, domains, and result counts. He emphasizes that AI agents require search engines that return exactly what they ask for, not what humans click on, and can handle thousands of results. The episode also previews Exa's new research endpoint for automated deep research.