Introduction: Choosing the right LLM for your task is one of the most impactful decisions you’ll make. Use a model that’s too small and you’ll get poor quality. Use one that’s too large and you’ll burn through budget while waiting for slow responses. The landscape changes constantly—new models launch monthly, pricing shifts, and capabilities evolve. […]
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Structured Generation Techniques: Getting Reliable JSON from LLMs
Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]
Read more →Privacy-Preserving AI: Techniques for Sensitive Data
Last year, we trained a model on customer data. A researcher showed they could reconstruct customer information from model outputs. After implementing privacy-preserving techniques across 10+ projects, I’ve learned how to protect sensitive data while enabling AI capabilities. Here’s the complete guide to privacy-preserving AI. Figure 1: Privacy-Preserving AI Techniques Overview Why Privacy-Preserving AI Matters: […]
Read more →The Evolution of .NET: Why Modern C# Development Feels Like a Different Language
If you’ve been writing C# for more than a decade, you’ve witnessed something remarkable: the language you learned in the early 2000s bears only a superficial resemblance to what we write today. Modern C# development feels like a different language entirely. C# Syntax Evolution: 2002 vs 2025 The Transformation Journey When .NET Framework first appeared, […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]
Read more →LLM Security Best Practices: Protecting AI Applications from Attacks
Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from […]
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