Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
Read more →Search Results for: name
Achieving DevOps Harmony: Building and Deploying .NET Applications with AWS Services
The Evolution of .NET Deployment on AWS After two decades of building enterprise applications, I’ve witnessed the transformation of deployment practices from manual FTP uploads to sophisticated CI/CD pipelines. When AWS introduced their native DevOps toolchain, it fundamentally changed how we approach .NET application delivery. The integration between CodeCommit, CodeBuild, CodePipeline, and ECR creates a […]
Read more →LLM Security: Understanding Prompt Injection, Jailbreaking, and Attack Vectors (Part 1 of 2)
A comprehensive guide to securing LLM applications against prompt injection, jailbreaking, and data exfiltration attacks. Includes production-ready defense implementations.
Read more →LLM Output Formatting: JSON Mode, Pydantic Parsing, and Template-Based Outputs
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 […]
Read more →Building Production AI Applications with .NET 8 and C# 12
When .NET 8 and C# 12 were released, I was skeptical. After 15 years building enterprise applications, I’d seen framework updates come and go. But this release changed everything for AI development. Let me show you how to build production AI applications with .NET 8 and C# 12—using actual C# code, not Python wrappers. Figure […]
Read more →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 →