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 […]
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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 →Async LLM Patterns: Concurrent Execution, Rate Limiting, and Task Queues for High-Throughput AI Applications
Introduction: LLM API calls are inherently I/O-bound—waiting for network responses dominates execution time. Async programming transforms this bottleneck into an opportunity for massive parallelism. Instead of waiting sequentially for each response, async patterns enable concurrent execution of hundreds of requests while efficiently managing resources. This guide covers practical async patterns for LLM applications: concurrent request […]
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 →Exploring Anaconda AI Navigator: A Comprehensive Guide for Windows Users
When Anaconda released their AI Navigator tool, I was skeptical. After two decades of building data science environments from scratch, managing conda environments manually, and wrestling with dependency conflicts across dozens of projects, I wondered if yet another GUI tool could actually solve the problems that have plagued Python development for years. After six months […]
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