Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with […]
Read more →Prompt Optimization: From Few-Shot to Automated Tuning
Introduction: Prompt engineering is both art and science—small changes in wording can dramatically affect LLM output quality. Systematic prompt optimization goes beyond trial and error to find prompts that consistently perform well. This guide covers proven optimization techniques: few-shot learning with carefully selected examples, chain-of-thought prompting for complex reasoning, structured output formatting, prompt compression for […]
Read more →Introduction to Site Reliability Engineering (SRE) in Azure: Achieving Higher Reliability with AKS and Essential Tools
In the fast-paced world of technology, ensuring the reliability of services is paramount for businesses to thrive. Site Reliability Engineering (SRE) has emerged as a discipline that combines software engineering and systems administration to create scalable and highly reliable software systems. In the Azure cloud environment, Azure Kubernetes Service (AKS) plays a pivotal role in […]
Read more →Model Context Protocol (MCP): Building AI-Tool Integrations That Scale
Introduction: The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI assistants to securely connect with external data sources and tools. Think of MCP as a universal adapter that lets AI models interact with your files, databases, APIs, and services through a standardized interface. Instead of building custom integrations for […]
Read more →Data Lakehouse Architecture: Bridging Data Lakes and Data Warehouses
After two decades of building data platforms, I’ve witnessed the pendulum swing between data lakes and data warehouses multiple times. Organizations would invest heavily in one approach, hit its limitations, then pivot to the other. The data lakehouse architecture represents something different—a genuine synthesis that addresses the fundamental trade-offs that forced us to choose between […]
Read more →LLM Cost Optimization: Model Routing, Token Reduction, and Budget Management (Part 2 of 2)
Introduction: LLM API costs can escalate quickly—a single GPT-4 call costs 100x more than GPT-4o-mini for the same tokens. Effective cost optimization requires a multi-pronged approach: intelligent model routing based on task complexity, aggressive caching for repeated queries, prompt optimization to reduce token usage, and batching to maximize throughput. This guide covers practical cost optimization […]
Read more →