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. […]
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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 →Vector Embeddings Deep Dive: From Theory to Production Search Systems
Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into […]
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 →Vector Search Optimization: The Complete Guide to Embeddings, Indexing, and Hybrid Search
Introduction: Vector search is the foundation of modern RAG systems, but naive implementations often deliver poor results. Optimizing vector search requires understanding embedding models, index types, query strategies, and reranking techniques. The difference between a basic similarity search and a well-tuned retrieval pipeline can be dramatic—both in relevance and latency. This guide covers practical vector […]
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 […]
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