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      <title>Pinecone Introduces Nexus Engine for Compiling Business Context into Structured Data for AI Agents</title>
      <link>https://www.infoq.com/news/2026/07/pinecon-nexus-knowledge-engine/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Retrieval-Augmented+Generation-news</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Now generally available, Pinecone Nexus is a "knowledge engine" for AI agents that transforms enterprise data into a structured layer agents can query directly. It enables teams to ingest and curate business context once for all, making it reusable across agents and reducing token costs while improving accuracy.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Enterprise</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>vector databases</category>
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      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Sat, 18 Jul 2026 14:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/pinecon-nexus-knowledge-engine/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Retrieval-Augmented+Generation-news</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-07-18T14:00:00Z</dc:date>
      <dc:identifier>/news/2026/07/pinecon-nexus-knowledge-engine/en</dc:identifier>
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    <item>
      <title>How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone</title>
      <link>https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Retrieval-Augmented+Generation-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/07/doordash-ai-ask-assistant/en/headerimage/generatedHeaderImage-1782515732223.jpg"/&gt;&lt;p&gt;DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-based tooling, and an intelligence layer with persistent consumer memory and live backend data. Early results show up to 24% higher checkout conversion, 17% larger baskets, and improved intent accuracy using memory-backed sessions.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Natural Language Processing</category>
      <category>Distributed Systems</category>
      <category>Microservices</category>
      <category>Retrieval-Augmented Generation</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Artificial Intelligence</category>
      <category>ChatBots</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Memory</category>
      <category>Prompt Engineering</category>
      <category>Platform Engineering</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 13 Jul 2026 14:08:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Retrieval-Augmented+Generation-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-07-13T14:08:00Z</dc:date>
      <dc:identifier>/news/2026/07/doordash-ai-ask-assistant/en</dc:identifier>
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