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    <title>AI Wisdom — Engineering Intelligent Systems</title>
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    <description>Architecture, models, and practical AI engineering insights for developers building real-world systems.</description>
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    <lastBuildDate>Thu, 16 Apr 2026 17:07:13 GMT</lastBuildDate>
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      <title><![CDATA[How to Build a Production-Ready AI System (Azure OpenAI + AI Search — Real Architecture)]]></title>
      <link>https://aiwisdom.dev/ai-architecture/building-a-production-ready-ai-system</link>
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      <description><![CDATA[Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.]]></description>
      <pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate>
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      <category><![CDATA[Azure-OpenAI]]></category>
      <category><![CDATA[AI-Search]]></category>
      <category><![CDATA[production]]></category>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[architecture]]></category>
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      <title><![CDATA[From Chatbot to Agent: Adding Tools, Memory, and Planning to a Simple Chat Interface]]></title>
      <link>https://aiwisdom.dev/experiments/from-chatbot-to-agent</link>
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      <description><![CDATA[A practical walkthrough of evolving a basic LLM chatbot into a capable agent — adding tool calling, persistent memory, and multi-step planning.]]></description>
      <pubDate>Sun, 15 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[experiments]]></category>
      <category><![CDATA[chatbot]]></category>
      <category><![CDATA[agents]]></category>
      <category><![CDATA[tool-calling]]></category>
      <category><![CDATA[memory]]></category>
      <category><![CDATA[planning]]></category>
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      <title><![CDATA[Structured Outputs from LLMs: JSON Mode, Function Calling, and Schema Enforcement]]></title>
      <link>https://aiwisdom.dev/engineering/structured-outputs-from-llms</link>
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      <description><![CDATA[Practical patterns for getting reliable structured data from LLMs — JSON mode, function calling, schema validation, and fallback strategies.]]></description>
      <pubDate>Sat, 14 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[structured-output]]></category>
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      <category><![CDATA[LLM]]></category>
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      <title><![CDATA[Token Economics: Understanding and Optimizing LLM Costs]]></title>
      <link>https://aiwisdom.dev/models/token-economics-understanding-and-optimizing-llm-costs</link>
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      <description><![CDATA[A practical guide to understanding token pricing, measuring real costs, and implementing optimization strategies — caching, prompt compression, model routing.]]></description>
      <pubDate>Thu, 12 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[models]]></category>
      <category><![CDATA[token-economics]]></category>
      <category><![CDATA[cost-optimization]]></category>
      <category><![CDATA[caching]]></category>
      <category><![CDATA[model-routing]]></category>
      <category><![CDATA[LLM]]></category>
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      <title><![CDATA[Vector Database Selection for Production RAG]]></title>
      <link>https://aiwisdom.dev/ai-architecture/vector-database-selection-for-production-rag</link>
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      <description><![CDATA[Cosmos DB, AI Search, Qdrant, Pinecone — benchmarks, cost, and operational complexity for production vector search.]]></description>
      <pubDate>Wed, 11 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai-architecture]]></category>
      <category><![CDATA[vector-db]]></category>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[Cosmos-DB]]></category>
      <category><![CDATA[Pinecone]]></category>
      <category><![CDATA[Qdrant]]></category>
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      <title><![CDATA[AI-Powered Code Review: Building a Review Bot That Actually Helps]]></title>
      <link>https://aiwisdom.dev/experiments/ai-powered-code-review</link>
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      <description><![CDATA[How to build an AI code review system that catches real issues — architecture, prompt design, GitHub integration, and practical lessons.]]></description>
      <pubDate>Sun, 08 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[experiments]]></category>
      <category><![CDATA[code-review]]></category>
      <category><![CDATA[AI]]></category>
      <category><![CDATA[GitHub]]></category>
      <category><![CDATA[automation]]></category>
      <category><![CDATA[prompt-design]]></category>
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      <title><![CDATA[Prompt Engineering as Software Engineering]]></title>
      <link>https://aiwisdom.dev/engineering/prompt-engineering-as-software-engineering</link>
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      <description><![CDATA[Version control, testing, parameterization, and CI pipelines for prompts — treating prompt engineering with the same rigor as application code.]]></description>
      <pubDate>Sat, 07 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[prompt-engineering]]></category>
      <category><![CDATA[CI]]></category>
      <category><![CDATA[version-control]]></category>
      <category><![CDATA[testing]]></category>
      <category><![CDATA[best-practices]]></category>
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      <title><![CDATA[LLM Evaluation Beyond Vibes]]></title>
      <link>https://aiwisdom.dev/models/llm-evaluation-beyond-vibes</link>
      <guid isPermaLink="true">https://aiwisdom.dev/models/llm-evaluation-beyond-vibes</guid>
      <description><![CDATA[Systematic approaches to evaluating LLM outputs — automated metrics, human evaluation frameworks, regression testing, and building evaluation pipelines.]]></description>
      <pubDate>Thu, 05 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[models]]></category>
      <category><![CDATA[evaluation]]></category>
      <category><![CDATA[metrics]]></category>
      <category><![CDATA[regression-testing]]></category>
      <category><![CDATA[LLM]]></category>
      <category><![CDATA[quality]]></category>
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      <title><![CDATA[Multi-Agent Architecture Patterns in Production]]></title>
      <link>https://aiwisdom.dev/ai-architecture/multi-agent-architecture-patterns</link>
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      <description><![CDATA[Orchestrator, supervisor, and swarm patterns for multi-agent systems with real trade-offs and failure modes.]]></description>
      <pubDate>Wed, 04 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai-architecture]]></category>
      <category><![CDATA[multi-agent]]></category>
      <category><![CDATA[orchestration]]></category>
      <category><![CDATA[architecture]]></category>
      <category><![CDATA[production]]></category>
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      <title><![CDATA[MCP Servers: Building Tool-Using AI Agents with the Model Context Protocol]]></title>
      <link>https://aiwisdom.dev/experiments/mcp-servers-building-tool-using-ai-agents</link>
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      <description><![CDATA[How MCP works, how to build MCP servers that expose tools to AI agents, and practical patterns for connecting LLMs to your systems.]]></description>
      <pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[experiments]]></category>
      <category><![CDATA[MCP]]></category>
      <category><![CDATA[tool-use]]></category>
      <category><![CDATA[agents]]></category>
      <category><![CDATA[Model-Context-Protocol]]></category>
      <category><![CDATA[integration]]></category>
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      <title><![CDATA[Building Reliable AI Agents with Semantic Kernel]]></title>
      <link>https://aiwisdom.dev/engineering/building-reliable-ai-agents-semantic-kernel</link>
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      <description><![CDATA[Plugin architecture, memory, planners, and error handling patterns for building production AI agents in .NET with Semantic Kernel.]]></description>
      <pubDate>Sat, 31 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[Semantic-Kernel]]></category>
      <category><![CDATA[agents]]></category>
      <category><![CDATA[dotnet]]></category>
      <category><![CDATA[plugins]]></category>
      <category><![CDATA[production]]></category>
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      <title><![CDATA[Small Language Models in Production]]></title>
      <link>https://aiwisdom.dev/models/small-language-models-in-production</link>
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      <description><![CDATA[When and how to use small language models like Phi, Gemma, and Mistral in production — quantization, deployment patterns, and latency-cost trade-offs.]]></description>
      <pubDate>Thu, 29 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[models]]></category>
      <category><![CDATA[SLM]]></category>
      <category><![CDATA[Phi]]></category>
      <category><![CDATA[Gemma]]></category>
      <category><![CDATA[Mistral]]></category>
      <category><![CDATA[quantization]]></category>
      <category><![CDATA[production]]></category>
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  <item>
      <title><![CDATA[Event-Driven AI: Building Async Pipelines for LLM Workloads]]></title>
      <link>https://aiwisdom.dev/ai-architecture/event-driven-ai-async-pipelines</link>
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      <description><![CDATA[Service Bus, Event Grid, and queue-based orchestration for AI tasks that don't belong in the request-response path.]]></description>
      <pubDate>Wed, 28 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai-architecture]]></category>
      <category><![CDATA[event-driven]]></category>
      <category><![CDATA[async]]></category>
      <category><![CDATA[Service-Bus]]></category>
      <category><![CDATA[Event-Grid]]></category>
      <category><![CDATA[queues]]></category>
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      <title><![CDATA[Building a Personal AI Knowledge Base]]></title>
      <link>https://aiwisdom.dev/experiments/building-a-personal-ai-knowledge-base</link>
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      <description><![CDATA[How to build a personal RAG system over your notes, bookmarks, and documents — using embeddings, vector search, and a conversational interface.]]></description>
      <pubDate>Sun, 25 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[experiments]]></category>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[knowledge-base]]></category>
      <category><![CDATA[embeddings]]></category>
      <category><![CDATA[vector-search]]></category>
      <category><![CDATA[personal-AI]]></category>
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      <title><![CDATA[Testing LLM-Powered Features Without Going Broke]]></title>
      <link>https://aiwisdom.dev/engineering/testing-llm-powered-features</link>
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      <description><![CDATA[Mock strategies, evaluation harnesses, snapshot testing, and cost-aware CI for LLM-integrated applications.]]></description>
      <pubDate>Sat, 24 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[testing]]></category>
      <category><![CDATA[LLM]]></category>
      <category><![CDATA[CI]]></category>
      <category><![CDATA[mocks]]></category>
      <category><![CDATA[evaluation]]></category>
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      <title><![CDATA[When to Fine-Tune vs Few-Shot vs RAG]]></title>
      <link>https://aiwisdom.dev/models/when-to-fine-tune-vs-few-shot-vs-rag</link>
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      <description><![CDATA[A decision framework for choosing between fine-tuning, few-shot prompting, and RAG for production LLM applications.]]></description>
      <pubDate>Thu, 22 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[models]]></category>
      <category><![CDATA[fine-tuning]]></category>
      <category><![CDATA[few-shot]]></category>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[decision-framework]]></category>
      <category><![CDATA[LLM]]></category>
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      <title><![CDATA[The AI Gateway Pattern: Why Every Production LLM Needs One]]></title>
      <link>https://aiwisdom.dev/ai-architecture/the-ai-gateway-pattern</link>
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      <description><![CDATA[API Management, rate limiting, semantic caching, and cost control with Azure APIM as an AI Gateway.]]></description>
      <pubDate>Wed, 21 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai-architecture]]></category>
      <category><![CDATA[API-gateway]]></category>
      <category><![CDATA[Azure-APIM]]></category>
      <category><![CDATA[cost-control]]></category>
      <category><![CDATA[caching]]></category>
      <category><![CDATA[production]]></category>
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      <title><![CDATA[Testing Autonomous Coding Agents: GitHub Copilot, Cursor, and Windsurf in Real Projects]]></title>
      <link>https://aiwisdom.dev/experiments/testing-autonomous-coding-agents</link>
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      <description><![CDATA[A hands-on experiment comparing autonomous coding agents on real engineering tasks — multi-file refactoring, bug fixing, and feature implementation.]]></description>
      <pubDate>Sun, 18 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[experiments]]></category>
      <category><![CDATA[coding-agents]]></category>
      <category><![CDATA[Copilot]]></category>
      <category><![CDATA[Cursor]]></category>
      <category><![CDATA[Windsurf]]></category>
      <category><![CDATA[benchmarks]]></category>
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      <title><![CDATA[Integrating Azure OpenAI with ASP.NET Core: A Production Guide]]></title>
      <link>https://aiwisdom.dev/engineering/integrating-azure-openai-with-aspnet-core</link>
      <guid isPermaLink="true">https://aiwisdom.dev/engineering/integrating-azure-openai-with-aspnet-core</guid>
      <description><![CDATA[SDK setup, retry policies, streaming responses, and structured outputs for Azure OpenAI in .NET production applications.]]></description>
      <pubDate>Sat, 17 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[Azure-OpenAI]]></category>
      <category><![CDATA[ASP.NET-Core]]></category>
      <category><![CDATA[dotnet]]></category>
      <category><![CDATA[SDK]]></category>
      <category><![CDATA[production]]></category>
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      <title><![CDATA[Claude vs GPT for Engineering Workflows]]></title>
      <link>https://aiwisdom.dev/models/claude-vs-gpt-for-engineering-workflows</link>
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      <description><![CDATA[A practical comparison of Claude and GPT models for real engineering tasks — code generation, debugging, architecture reviews, and documentation.]]></description>
      <pubDate>Thu, 15 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[models]]></category>
      <category><![CDATA[Claude]]></category>
      <category><![CDATA[GPT]]></category>
      <category><![CDATA[comparison]]></category>
      <category><![CDATA[engineering]]></category>
      <category><![CDATA[benchmarks]]></category>
    </item>
  <item>
      <title><![CDATA[Designing RAG Systems That Actually Scale]]></title>
      <link>https://aiwisdom.dev/ai-architecture/designing-rag-systems-that-actually-scale</link>
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      <description><![CDATA[Chunking strategies, embedding pipelines, retrieval patterns, and when RAG breaks down in production systems.]]></description>
      <pubDate>Wed, 14 Jan 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai-architecture]]></category>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[vector-db]]></category>
      <category><![CDATA[LLM]]></category>
      <category><![CDATA[production]]></category>
      <category><![CDATA[embeddings]]></category>
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