Production-grade observability for AI/LLM applications. Learn how to implement comprehensive monitoring with logs, metrics, distributed tracing, cost attribution, and latency tracking using OpenTelemetry, Prometheus, and Grafana.
Comprehensive guide to reducing latency in AI applications. Learn batching strategies, semantic caching with Redis, edge deployment, prompt compression, streaming responses, and model selection for sub-second response times.
Production-grade strategies for safely deploying new AI model versions. Learn traffic splitting, quality monitoring, automated rollbacks, A/B testing frameworks, and Kubernetes-based canary deployments for GPT-5, Claude, and self-hosted models.
Comprehensive TCO analysis for AI infrastructure decisions. Compare hosted models (GPT-5, Claude Opus 4.1) vs self-hosted open-weight models (Llama 4, Mistral). Break-even calculations, privacy considerations, and decision framework for enterprises.
Technical guide to implementing RAG systems with vector databases. Compare Pinecone, Weaviate, Milvus, and pgvector. Learn about embeddings, similarity search, and production architecture.
Technical comparison of fine-tuning and prompt engineering for LLM customization. Learn when to use each approach, implementation details, costs, and performance trade-offs.
Fine-TuningPrompt EngineeringModel CustomizationLLM Training
