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 →

Prompt Versioning and A/B Testing: Engineering Discipline for Prompt Management

Introduction: Prompts are code—they define your application’s behavior and should be managed with the same rigor as source code. Yet many teams treat prompts as ad-hoc strings scattered throughout their codebase, making it impossible to track changes, compare versions, or systematically improve performance. This guide covers practical prompt management: version control systems for prompts, A/B […]

Read more →

Azure OpenAI Service with Python: Building Enterprise AI Applications

After spending two decades building enterprise applications, I’ve watched countless “revolutionary” technologies come and go. But Azure OpenAI Service represents something genuinely different—a managed platform that brings the power of GPT-4 and other foundation models into the enterprise with the security, compliance, and operational controls that production systems demand. Here’s what I’ve learned from integrating […]

Read more →

LLM Guardrails and Safety: Protecting Your AI Application from Attacks

Introduction: Deploying LLMs in production without guardrails is like driving without seatbelts—it might work fine until it doesn’t. Users will try to jailbreak your system, inject malicious prompts, extract training data, and push your model into generating harmful content. Guardrails are the safety layer between raw LLM capabilities and your users. This guide covers implementing […]

Read more →