Introduction: Processing thousands or millions of items through LLMs requires different patterns than single-request applications. Naive sequential processing is too slow, while uncontrolled parallelism hits rate limits and wastes money on retries. This guide covers production batch processing patterns: chunking strategies, parallel execution with rate limiting, progress tracking, checkpoint/resume for long jobs, cost estimation, and […]
Read more →Category: Technology Engineering
Technology Engineering
Vector Search Optimization: The Complete Guide to Embeddings, Indexing, and Hybrid Search
Introduction: Vector search is the foundation of modern RAG systems, but naive implementations often deliver poor results. Optimizing vector search requires understanding embedding models, index types, query strategies, and reranking techniques. The difference between a basic similarity search and a well-tuned retrieval pipeline can be dramatic—both in relevance and latency. This guide covers practical vector […]
Read more →Building Production AI Applications with .NET 8 and C# 12
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 […]
Read more →LLM Output Formatting: JSON Mode, Pydantic Parsing, and Template-Based Outputs
Introduction: LLM outputs are inherently unstructured text, but applications need structured data—JSON objects, typed responses, specific formats. Getting reliable structured output requires careful prompt engineering, output parsing, validation, and error recovery. This guide covers practical output formatting techniques: JSON mode and structured outputs, Pydantic-based parsing, format enforcement with retries, template-based formatting, and strategies for handling […]
Read more →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 […]
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