When .NET 8 and C# 12 were released, I was skeptical. After 15 years building enterprise applications, I’d seen framework updates come and go. But this release changed everything for AI development. Let me show you how to build production AI applications with .NET 8 and C# 12—using actual C# code, not Python wrappers. Figure […]
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LLM Chain Composition: Building Complex AI Workflows with Sequential, Parallel, and Conditional Patterns
Introduction: Complex LLM applications rarely consist of a single prompt—they chain multiple steps together, each building on the previous output. Chain composition enables sophisticated workflows: retrieval-augmented generation, multi-step reasoning, iterative refinement, and conditional branching. Understanding how to compose chains effectively is essential for building production LLM systems. This guide covers practical chain patterns: sequential chains, […]
Read more →Building LLM Agents with Tools: From Simple Loops to Production Systems
Introduction: LLM agents extend language models beyond text generation into autonomous action. By connecting LLMs to tools—web search, code execution, APIs, databases—agents can gather information, perform calculations, and interact with external systems. This guide covers building tool-using agents from scratch: defining tools with schemas, implementing the reasoning loop, handling tool execution, managing conversation state, and […]
Read more →Prompt Template Management: Engineering Discipline for LLM Prompts
Introduction: Prompts are the interface between your application and LLMs. As applications grow, managing prompts becomes challenging—they’re scattered across code, hard to version, and difficult to test. A prompt template system brings order to this chaos. It separates prompt logic from application code, enables versioning and A/B testing, and makes prompts reusable across different contexts. […]
Read more →Document Processing with LLMs: Enterprise Parsing, Chunking, and Extraction (Part 2 of 2)
Introduction: Processing documents with LLMs unlocks powerful capabilities: extracting structured data from unstructured text, summarizing lengthy reports, answering questions about document content, and transforming documents between formats. However, effective document processing requires more than just sending text to an LLM—it demands careful parsing, intelligent chunking, and strategic prompting. This guide covers practical document processing patterns: […]
Read more →Multi-Cloud AI Strategies: Avoiding Vendor Lock-in
Multi-cloud AI strategies prevent vendor lock-in and optimize costs. After implementing multi-cloud for 20+ AI projects, I’ve learned what works. Here’s the complete guide to multi-cloud AI strategies. Figure 1: Multi-Cloud AI Architecture Why Multi-Cloud for AI Multi-cloud strategies offer significant advantages: Vendor independence: Avoid lock-in to single cloud provider Cost optimization: Use best pricing […]
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