<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Reranking – M365.FM</title><link>https://www.m365.fm/entity/reranking/</link><description>Reranking is a retrieval step in which a second model rescores the top candidates returned by a search so the most relevant results are passed on, for example to a language model in a RAG pipeline.</description><language>en</language><item><title>Why Pure Vector Search Fails Enterprise AI (And How to Fix It)</title><link>https://www.m365.fm/community/u/mirko-p/blog/why-pure-vector-search-fails-enterprise-ai-and-how-to-fix-it/</link><guid>https://www.m365.fm/community/u/mirko-p/blog/why-pure-vector-search-fails-enterprise-ai-and-how-to-fix-it/</guid><pubDate>Wed, 26 Aug 2026 13:09:05 -0000</pubDate><category>article</category><description>When you start deploying generative AI across your organization, you quickly realize that standard Retrieval-Augmented Generation (RAG) setups lean heavily on pure …</description></item><item><title>HNSW vs DiskANN for Vector Search in Azure AI Search</title><link>https://www.m365.fm/show/episodes/the-billion-vector-problem-hnsw-vs-diskann-in-azure-ai-search/</link><guid>https://www.m365.fm/show/episodes/the-billion-vector-problem-hnsw-vs-diskann-in-azure-ai-search/</guid><pubDate>Mon, 08 Jun 2026 00:00:00 -0000</pubDate><category>episode</category><description>The explosion of AI-powered applications has created a new challenge: how do you efficiently search through billions of vector embeddings without exploding infrastructure costs? In this episode, we explore the “billion-vector problem” and compare two leading…</description></item><item><title>Scaling Microsoft Copilot to 35 Million Pages: The Epstein Files</title><link>https://www.m365.fm/show/episodes/i-engineered-copilot-for-35-million-pages-the-epstein-files-challenge/</link><guid>https://www.m365.fm/show/episodes/i-engineered-copilot-for-35-million-pages-the-epstein-files-challenge/</guid><pubDate>Sun, 07 Jun 2026 00:00:00 -0000</pubDate><category>episode</category><description>In this episode, Mirko Peters explores one of the most ambitious experiments in enterprise AI: engineering Microsoft Copilot to analyze and reason across more than 35 million pages of highly complex and interconnected information, inspired by the scale and…</description></item><item><title>Improve Microsoft Copilot Accuracy Beyond Vector Search</title><link>https://www.m365.fm/show/episodes/vector-search-is-not-a-strategy-the-new-standard-for-copilot-accuracy/</link><guid>https://www.m365.fm/show/episodes/vector-search-is-not-a-strategy-the-new-standard-for-copilot-accuracy/</guid><pubDate>Fri, 01 May 2026 00:00:00 -0000</pubDate><category>episode</category><description>This episode explains that vector search on its own is not a complete or reliable strategy for building accurate AI systems like Copilot. While many organizations rely on embeddings and vector databases to retrieve information, this approach often leads to…</description></item><item><title>Fix Common Azure AI Foundry Project Failures</title><link>https://www.m365.fm/show/episodes/the-azure-ai-foundry-trap-why-most-fail-fast/</link><guid>https://www.m365.fm/show/episodes/the-azure-ai-foundry-trap-why-most-fail-fast/</guid><pubDate>Mon, 03 Nov 2025 00:00:00 -0000</pubDate><category>episode</category><description>In this episode we walk through what really happens when Azure AI Foundry doesn’t behave the way you expect, especially when the Agent Service or deployments start acting up. Azure AI Foundry is supposed to feel smooth, almost invisible, tying together OpenAI…</description></item></channel></rss>