Introduction: LLM applications face unique security challenges—prompt injection, data leakage, jailbreaking, and harmful content generation. Traditional security measures don’t address these AI-specific threats. This guide covers defensive techniques for production LLM systems: input sanitization, prompt injection detection, output filtering, rate limiting, content moderation, and audit logging. These patterns help you build LLM applications that are […]
Read more →Month: March 2024
Advanced RAG Patterns: From Naive Retrieval to Production-Grade Systems
Introduction: Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to private or current information. By retrieving relevant documents and including them in the prompt, RAG grounds LLM responses in factual content, reducing hallucinations and enabling knowledge that wasn’t in the training data. But naive RAG implementations often disappoint—the […]
Read more →Introduction to Generative AI: A Comprehensive Guide
The first time I watched a generative model produce coherent text from a simple prompt, I knew we had crossed a threshold that would reshape how we build software. After two decades of working with various AI and ML systems, from rule-based expert systems to deep learning pipelines, I can say with confidence that generative […]
Read more →Embedding Strategies: Model Selection, Batching, and Long Document Handling
Introduction: Embeddings are the foundation of semantic search, RAG systems, and similarity-based applications. Choosing the right embedding model and strategy significantly impacts retrieval quality, latency, and cost. Different models excel at different tasks—some optimize for semantic similarity, others for retrieval, and some for specific domains. This guide covers practical embedding strategies: model selection based on […]
Read more →Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was […]
Read more →Scaling Up Your Pods: How Horizontal Pod Autoscaling Wins
After two decades of managing containerized workloads across production environments, I’ve come to appreciate that the difference between a good Kubernetes deployment and a great one often comes down to how intelligently it responds to changing demand. Horizontal Pod Autoscaling (HPA) represents one of those fundamental capabilities that separates reactive operations from proactive infrastructure management. […]
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