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 →Category: Technology Engineering
Technology Engineering
The .NET Renaissance: How C# 13 and .NET 9 Are Redefining What Modern Development Looks Like
After two decades of building enterprise applications on the Microsoft stack, I’ve witnessed every major evolution of .NET—from the original Framework through the tumultuous transition to Core, and now to the unified platform that .NET 9 represents. What strikes me most about this release isn’t any single feature, but rather how it crystallizes Microsoft’s vision […]
Read more →Advanced Retrieval Strategies for RAG: The Complete Guide to Dense, Hybrid, and Multi-Stage Search
Introduction: Retrieval is the foundation of RAG systems—the quality of retrieved documents directly impacts generation quality. Different retrieval strategies excel in different scenarios: dense retrieval captures semantic similarity, sparse retrieval handles exact keyword matches, and hybrid approaches combine both. This guide covers advanced retrieval techniques: embedding-based dense retrieval, BM25 and sparse methods, hybrid search strategies, […]
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, […]
Read more →Prompt Optimization Strategies: From Structure to Automatic Refinement
Introduction: Prompt optimization is the systematic process of improving prompts to achieve better LLM outputs—higher accuracy, more consistent formatting, reduced latency, and lower costs. Unlike ad-hoc prompt engineering, optimization treats prompts as artifacts that can be measured, tested, and iteratively improved. This guide covers the techniques that make prompts more effective: structural patterns that improve […]
Read more →Building Production RAG Applications with LangChain: From Document Ingestion to Conversational AI
Introduction: LangChain has emerged as the dominant framework for building production Retrieval-Augmented Generation (RAG) applications, providing abstractions for document loading, text splitting, embedding, vector storage, and retrieval chains. By late 2023, LangChain reached production maturity with improved stability, better documentation, and enterprise-ready features. After deploying LangChain-based RAG systems across multiple organizations, I’ve found that its […]
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