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        <title>The Agentset Blog</title>
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        <description>Stay informed with product updates, company news, and insights on how to sell smarter at your company.</description>
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        <copyright>All rights reserved 2026</copyright>
        <item>
            <title><![CDATA[RAG for the AI SDK]]></title>
            <link>http://localhost:3000/blog/rag-for-the-ai-sdk</link>
            <guid>rag-for-the-ai-sdk</guid>
            <pubDate>Fri, 27 Mar 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[A practical guide to implementing retrieval-augmented generation with the AI SDK — from ingesting documents into vectors to retrieving context at query time.]]></content:encoded>
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        <item>
            <title><![CDATA[Gemini 2 Is the Top Model for Embeddings]]></title>
            <link>http://localhost:3000/blog/gemini-2-embedding</link>
            <guid>gemini-2-embedding</guid>
            <pubDate>Wed, 11 Mar 2026 10:19:00 GMT</pubDate>
            <content:encoded><![CDATA[Google released Gemini Embedding 2, their first natively multimodal embedding model. We ran it against 17 models across 7 datasets. It takes #1 with 1605 Elo, but the top three are within 18 points of each other.]]></content:encoded>
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        <item>
            <title><![CDATA[GPT-5.4: OpenAI's RAG Regression]]></title>
            <link>http://localhost:3000/blog/gpt-5.4-rag-regression</link>
            <guid>gpt-5.4-rag-regression</guid>
            <pubDate>Sun, 08 Mar 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[OpenAI released GPT-5.4 with a Pro variant for extended reasoning. We tested both on our LLM-for-RAG leaderboard across three workloads.]]></content:encoded>
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        <item>
            <title><![CDATA[Zembed-1: The Current Best Embedding Model]]></title>
            <link>http://localhost:3000/blog/zembed-1</link>
            <guid>zembed-1</guid>
            <pubDate>Mon, 02 Mar 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[ZeroEntropy released zembed-1, a 4B embedding model distilled from zerank-2 reranker. We tested it to see how it performs on real retrieval tasks.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Claude Sonnet 4.6 for RAG]]></title>
            <link>http://localhost:3000/blog/sonnet-4.6-in-rag</link>
            <guid>sonnet-4.6-in-rag</guid>
            <pubDate>Wed, 18 Feb 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We evaluated Claude Sonnet 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks compared to frontier models.]]></content:encoded>
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        <item>
            <title><![CDATA[Voyage 4: Evaluation Notes]]></title>
            <link>http://localhost:3000/blog/voyage-4</link>
            <guid>voyage-4</guid>
            <pubDate>Mon, 09 Feb 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Claude Opus 4.6 Performance in RAG]]></title>
            <link>http://localhost:3000/blog/opus-4.6-in-rag</link>
            <guid>opus-4.6-in-rag</guid>
            <pubDate>Fri, 06 Feb 2026 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We evaluated Claude Opus 4.6 in a RAG setup across factual retrieval, synthesis, and scientific tasks versus 11 frontier models.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[How to Detect Hallucinations in RAG]]></title>
            <link>http://localhost:3000/blog/how-to-detect-hallucinations-in-rag</link>
            <guid>how-to-detect-hallucinations-in-rag</guid>
            <pubDate>Mon, 26 Jan 2026 10:00:00 GMT</pubDate>
            <content:encoded><![CDATA[RAG helps, but hallucinations still happen. We tested four detection methods to find the best for production—comparing accuracy, latency, and cost.]]></content:encoded>
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        <item>
            <title><![CDATA[Multimodal vs Text Embeddings: Performance Comparison]]></title>
            <link>http://localhost:3000/blog/multimodal-vs-text-embeddings</link>
            <guid>multimodal-vs-text-embeddings</guid>
            <pubDate>Thu, 25 Dec 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We compared a text-based and a multimodal embedding pipeline across text, tables, and charts to see where multimodal actually helps.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Gemini 3 Flash: A strong factual RAG model]]></title>
            <link>http://localhost:3000/blog/gemini-3-flash</link>
            <guid>gemini-3-flash</guid>
            <pubDate>Thu, 18 Dec 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We evaluated Gemini 3 Flash in a RAG setup to understand where it excels and where it falls short—focusing on factual retrieval and grounding.]]></content:encoded>
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        <item>
            <title><![CDATA[Cohere Rerank 4: A real upgrade over 3.5]]></title>
            <link>http://localhost:3000/blog/cohere-reranker-v4</link>
            <guid>cohere-reranker-v4</guid>
            <pubDate>Sat, 13 Dec 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We benchmarked Cohere Rerank 4 Pro and Fast against v3.5 and other rerankers under the same RAG pipeline.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[GPT-5.2 RAG Performance: We Tested It]]></title>
            <link>http://localhost:3000/blog/gpt5.2-on-rag</link>
            <guid>gpt5.2-on-rag</guid>
            <pubDate>Fri, 12 Dec 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We plugged GPT-5.2 into our LLM RAG leaderboard and compared it against nine other frontier models under the same RAG pipeline.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Best Vector Databases for RAG]]></title>
            <link>http://localhost:3000/blog/best-vector-db-for-rag</link>
            <guid>best-vector-db-for-rag</guid>
            <pubDate>Fri, 05 Dec 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We reviewed seven popular vector databases to understand how they differ in deployment, cost, and where they fit in real RAG systems.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Opus 4.5 is the new best model for RAG]]></title>
            <link>http://localhost:3000/blog/opus-4.5-eval</link>
            <guid>opus-4.5-eval</guid>
            <pubDate>Tue, 25 Nov 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[An evaluation of Opus 4.5 inside a real retrieval setup, compared against Gemini 3 Pro and GPT 5.1 across five behaviors that matter for RAG.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Gemini 3 vs GPT 5.1 for RAG]]></title>
            <link>http://localhost:3000/blog/gemini-3-vs-gpt5.1</link>
            <guid>gemini-3-vs-gpt5.1</guid>
            <pubDate>Wed, 19 Nov 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We tested Gemini 3 inside an actual retrieval setup and compared it directly with GPT-5.1 across five areas that matter for RAG.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Embedding models have converged]]></title>
            <link>http://localhost:3000/blog/embedding-models-converged</link>
            <guid>embedding-models-converged</guid>
            <pubDate>Sun, 16 Nov 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We compared 13 embedding models across 8 datasets using an LLM judge and ELO scoring. The result: almost all of them perform in the same narrow band.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Best Reranker for RAG: We tested the top models]]></title>
            <link>http://localhost:3000/blog/best-reranker</link>
            <guid>best-reranker</guid>
            <pubDate>Fri, 07 Nov 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We benchmarked eight leading rerankers to find which performs best for real-world RAG pipelines—comparing speed, accuracy, and relevance.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Cohere vs ZeRank: Which Reranker Actually Performs Better?]]></title>
            <link>http://localhost:3000/blog/cohere-vs-zerank-comparison</link>
            <guid>cohere-vs-zerank-comparison</guid>
            <pubDate>Mon, 27 Oct 2025 12:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We compared Cohere v3.5 and ZeRank-1 in a RAG pipeline using a BEIR subset and a custom dataset — analyzing accuracy, latency, and LLM preference.]]></content:encoded>
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        <item>
            <title><![CDATA[Is RAG Dead?]]></title>
            <link>http://localhost:3000/blog/is-rag-dead</link>
            <guid>is-rag-dead</guid>
            <pubDate>Tue, 15 Apr 2025 09:30:00 GMT</pubDate>
            <content:encoded><![CDATA[OpenAI released the GPT 4.1 models supporting 1M token context window. Gemini supports up to 10M tokens in research. Is the RAG era over?]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Building Effective RAG Pipelines: A Practical Guide]]></title>
            <link>http://localhost:3000/blog/building-effective-rag-pipelines-practical-guide</link>
            <guid>building-effective-rag-pipelines-practical-guide</guid>
            <pubDate>Thu, 01 May 2025 10:30:00 GMT</pubDate>
            <content:encoded><![CDATA[Learn how to design and implement robust retrieval-augmented generation (RAG) pipelines, from document processing to retrieval optimization.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Automate Business Workflows with AI Agents]]></title>
            <link>http://localhost:3000/blog/automate-business-workflows-with-ai-agents</link>
            <guid>automate-business-workflows-with-ai-agents</guid>
            <pubDate>Tue, 25 Mar 2025 10:30:00 GMT</pubDate>
            <content:encoded><![CDATA[Discover how AI agents can transform business operations by automating complex workflows, reducing manual effort, and improving efficiency.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[Building a Proof-of-Concept RAG System in an Afternoon]]></title>
            <link>http://localhost:3000/blog/building-a-proof-of-concept-rag-system-in-an-afternoon</link>
            <guid>building-a-proof-of-concept-rag-system-in-an-afternoon</guid>
            <pubDate>Mon, 10 Mar 2025 13:30:00 GMT</pubDate>
            <content:encoded><![CDATA[A practical guide to quickly building a functional retrieval-augmented generation system to demonstrate the value of AI-powered document search.]]></content:encoded>
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        </item>
        <item>
            <title><![CDATA[The Art of Document Chunking for LLM Applications]]></title>
            <link>http://localhost:3000/blog/the-art-of-document-chunking-for-llm-applications</link>
            <guid>the-art-of-document-chunking-for-llm-applications</guid>
            <pubDate>Tue, 25 Feb 2025 14:45:00 GMT</pubDate>
            <content:encoded><![CDATA[Explore the nuances of effective document chunking strategies for retrieval-augmented generation systems and how they impact LLM performance.]]></content:encoded>
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        <item>
            <title><![CDATA[Parsing PDF Documents at Scale]]></title>
            <link>http://localhost:3000/blog/parsing-pdf-documents-at-scale</link>
            <guid>parsing-pdf-documents-at-scale</guid>
            <pubDate>Mon, 10 Feb 2025 09:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Learn strategies and techniques to efficiently extract structured information from large volumes of PDF documents for use in AI applications.]]></content:encoded>
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