Use MERGE (upsert) for safe, rerunnable data pipelines that handle duplicates gracefully.
Read more →Tag: LLM
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 →Claude API Deep Dive: Building with Anthropic’s Models
A comprehensive guide to the Anthropic Claude API covering Claude 3.5 Sonnet, tool use, vision, computer use, and production best practices.
Read more →The Complete Guide to RAG Architecture: From Fundamentals to Production
Master Retrieval-Augmented Generation (RAG) with this expert-level guide. Learn about RAG types (Naive, Advanced, Modular, Agentic), chunking strategies, embedding models, vector databases, hybrid retrieval, and production best practices with high-quality architecture diagrams.
Read more →Prompt Templates and Versioning: Building Maintainable LLM Applications
Introduction: Production LLM applications need structured prompt management—not ad-hoc string concatenation scattered across code. Prompt templates provide reusable, parameterized prompts with consistent formatting. Versioning enables A/B testing, rollbacks, and tracking which prompts produced which results. This guide covers practical prompt template patterns: template engines and variable substitution, prompt registries, version control strategies, A/B testing frameworks, […]
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