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    <title>Nikhil Sachan | Blog</title>
    <description>Writing from Nikhil Sachan — Full Stack Developer building production-ready apps with Next.js, Node.js, and cloud. Notes on scalable systems, SaaS, AI integration, and shipping real products.</description>
    <link>https://blog.nikhilsachan.com</link>
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    <lastBuildDate>Thu, 04 Jun 2026 22:31:47 GMT</lastBuildDate>
    
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      <title><![CDATA[Agentic RAG: Multi-Agent Systems, Planning, and Tool Integration]]></title>
      <description><![CDATA[How agentic RAG combines retrieval-augmented generation with autonomous agents — ReAct patterns, chain-of-thought planning, memory systems, and building multi-agent RAG pipelines.]]></description>
      <link>https://blog.nikhilsachan.com/blog/agentic-rag-multi-agent-systems</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>AI</category>
      <category>LLM</category>
      <category>LangChain</category>
    </item>
    <item>
      <title><![CDATA[Building a Production-Ready RAG System: From Prototype to Deployment]]></title>
      <description><![CDATA[A complete guide to building production RAG systems — tech stack selection, data ingestion pipelines, chunking strategies, evaluation frameworks, and deployment architecture with code examples.]]></description>
      <link>https://blog.nikhilsachan.com/blog/building-production-rag-system</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>Production</category>
      <category>LangChain</category>
      <category>Architecture</category>
    </item>
    <item>
      <title><![CDATA[The Complete Guide to Retrieval-Augmented Generation (RAG)]]></title>
      <description><![CDATA[Everything you need to know about RAG — from fundamentals and architecture to production deployment. The definitive guide for developers building AI systems with retrieval-augmented generation.]]></description>
      <link>https://blog.nikhilsachan.com/blog/complete-guide-to-rag</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>AI</category>
      <category>LLM</category>
      <category>Machine Learning</category>
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    <item>
      <title><![CDATA[RAG Architecture Deep Dive: Embeddings, Vector Databases, and Retrieval]]></title>
      <description><![CDATA[A technical deep dive into RAG architecture — embeddings models, vector database comparison (FAISS, Pinecone, Weaviate, Chroma), retrieval strategies, and system design patterns.]]></description>
      <link>https://blog.nikhilsachan.com/blog/rag-architecture-deep-dive</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>Vector Database</category>
      <category>Architecture</category>
      <category>AI</category>
    </item>
    <item>
      <title><![CDATA[RAG in the Real World: Case Studies and Implementation Patterns]]></title>
      <description><![CDATA[Real-world RAG implementations across industries — customer support AI, internal knowledge assistants, legal document search, medical AI, and coding assistants with lessons learned.]]></description>
      <link>https://blog.nikhilsachan.com/blog/rag-case-studies-real-world</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>AI</category>
      <category>Production</category>
      <category>LLM</category>
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    <item>
      <title><![CDATA[RAG Fundamentals: How Retrieval-Augmented Generation Works]]></title>
      <description><![CDATA[A beginner-friendly deep dive into how RAG works — the retriever-generator pattern, embeddings, and building your first RAG chatbot with practical code examples.]]></description>
      <link>https://blog.nikhilsachan.com/blog/rag-fundamentals</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>AI</category>
      <category>LLM</category>
      <category>Machine Learning</category>
    </item>
    <item>
      <title><![CDATA[RAG vs Fine-Tuning: When to Use Each (and When Not To)]]></title>
      <description><![CDATA[A practical comparison of RAG and fine-tuning — ideal use cases, anti-patterns, cost analysis, and a decision framework to help you choose the right approach for your AI application.]]></description>
      <link>https://blog.nikhilsachan.com/blog/rag-vs-fine-tuning</link>
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      <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
      <category>RAG</category>
      <category>LLM</category>
      <category>AI</category>
      <category>Machine Learning</category>
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