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    <guid>https://Azurefixes.com/blog/01-production-ai-on-azure</guid>
    <title>Production AI on Azure: What Actually Breaks at Scale</title>
    <link>https://Azurefixes.com/blog/01-production-ai-on-azure</link>
    <description>Deploying Azure OpenAI in a demo is easy. Running it in production at scale is a different problem. Here are the seven failure modes we hit repeatedly — and how to fix them before they hit you.</description>
    <pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>OpenAI</category><category>Production</category><category>AIEngineering</category>
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    <guid>https://Azurefixes.com/blog/02-azure-openai-architecture</guid>
    <title>Azure OpenAI Reference Architecture: 3 Deployment Topologies</title>
    <link>https://Azurefixes.com/blog/02-azure-openai-architecture</link>
    <description>Not every Azure OpenAI deployment should look the same. Direct API, APIM gateway, and private VNet topologies each solve a different set of requirements. Here is when to use each one.</description>
    <pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>OpenAI</category><category>Architecture</category><category>APIM</category>
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    <guid>https://Azurefixes.com/blog/03-rag-chunking-strategies</guid>
    <title>Building RAG on Azure: Chunking Strategies That Actually Work</title>
    <link>https://Azurefixes.com/blog/03-rag-chunking-strategies</link>
    <description>Chunking is the step most teams rush past when building RAG systems. The wrong chunking strategy makes your search retrieve the right document but the wrong passage — and the LLM hallucinates the rest. Here is what works.</description>
    <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>RAG</category><category>AISearch</category><category>AIEngineering</category>
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    <guid>https://Azurefixes.com/blog/04-azure-ai-search-hybrid</guid>
    <title>Azure AI Search: Why Hybrid Search Beats Pure Vector</title>
    <link>https://Azurefixes.com/blog/04-azure-ai-search-hybrid</link>
    <description>Pure vector search sounds like the obvious choice for RAG. In practice, it misses exact matches, product codes, and proper nouns that keyword search catches trivially. Hybrid search using RRF consistently outperforms either approach alone.</description>
    <pubDate>Thu, 05 Feb 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>AISearch</category><category>RAG</category><category>Vector</category>
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    <guid>https://Azurefixes.com/blog/05-ai-observability</guid>
    <title>AI Observability: The 4 Signals You Are Not Tracking</title>
    <link>https://Azurefixes.com/blog/05-ai-observability</link>
    <description>Most teams instrument their AI apps with latency and error rate dashboards. Those are necessary but not sufficient. The failures that hurt production AI are invisible to standard APM tooling. Here are the four signals that actually matter.</description>
    <pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>Observability</category><category>OpenAI</category><category>MLOps</category>
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    <guid>https://Azurefixes.com/blog/06-prompt-versioning</guid>
    <title>Prompt Engineering on Azure: How to Version Your Prompts</title>
    <link>https://Azurefixes.com/blog/06-prompt-versioning</link>
    <description>Prompts are code. They need version control, testing, deployment pipelines, and rollback strategies. Most teams treat them like config files and wonder why their AI quality is inconsistent. Here is how to manage prompts properly.</description>
    <pubDate>Wed, 04 Mar 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>Prompts</category><category>AIEngineering</category><category>DevOps</category>
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    <guid>https://Azurefixes.com/blog/07-ai-security-prompt-injection</guid>
    <title>AI Security: 7 Prompt Injection Patterns You Need to Know</title>
    <link>https://Azurefixes.com/blog/07-ai-security-prompt-injection</link>
    <description>Prompt injection is the SQL injection of the AI era. Unlike traditional vulnerabilities, it requires no code exploit — just carefully crafted text. Here are the seven patterns attackers use and the Azure defenses that stop them.</description>
    <pubDate>Wed, 18 Mar 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>Security</category><category>OpenAI</category><category>PromptInjection</category>
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    <guid>https://Azurefixes.com/blog/09-azure-ai-agents</guid>
    <title>Azure AI Agents: Azure AI Agent Service vs Semantic Kernel vs LangChain</title>
    <link>https://Azurefixes.com/blog/09-azure-ai-agents</link>
    <description>Three serious options exist for building AI agents on Azure, and they solve different problems. Azure AI Agent Service is fully managed but opinionated. Semantic Kernel is Microsoft-native and enterprise-ready. LangChain is flexible but complex. Here is how to choose.</description>
    <pubDate>Thu, 16 Apr 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>Agents</category><category>SemanticKernel</category><category>LangChain</category><category>Architecture</category>
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    <guid>https://Azurefixes.com/blog/10-vector-search-comparison</guid>
    <title>Vector Search on Azure: AI Search vs Cosmos DB vs PostgreSQL</title>
    <link>https://Azurefixes.com/blog/10-vector-search-comparison</link>
    <description>Three Azure services now offer serious vector search capabilities. Azure AI Search, Cosmos DB for NoSQL, and Azure Database for PostgreSQL with pgvector each have a distinct sweet spot. Here is which to use and when.</description>
    <pubDate>Thu, 30 Apr 2026 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>Azure</category><category>AI</category><category>Vector</category><category>AISearch</category><category>CosmosDB</category><category>PostgreSQL</category><category>Architecture</category>
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    <guid>https://Azurefixes.com/blog/ai-observability-when-everything-looks-healthy</guid>
    <title>AI Observability: When Everything Looks Healthy But AI Is Wrong</title>
    <link>https://Azurefixes.com/blog/ai-observability-when-everything-looks-healthy</link>
    <description>Your AI app can fail even when nothing is technically broken. The server is healthy, the API is fast, the dashboards are green — but your chatbot still gives a completely wrong answer. That is the weird thing about GenAI systems.</description>
    <pubDate>Sun, 21 Dec 2025 00:00:00 GMT</pubDate>
    <author>address@yoursite.com (NVN)</author>
    <category>AI</category><category>GenerativeAI</category><category>LLM</category><category>AIEngineering</category><category>MLOps</category><category>Observability</category>
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