Observability for AI Apps: Telemetry, Evaluation, Safety, and Cost Optimization for Reliable LLMOps Observability for AI apps is the discipline of making complex AI systems—LLMs, RAG pipelines, agents, and tool-using…
RAG vs CAG: Retrieval‑Augmented Generation vs Context‑Augmented Generation Explained “RAG vs CAG” compares two complementary patterns for grounding large language models (LLMs) with reliable information. RAG (Retrieval‑Augmented Generation) retrieves relevant…
What Is MCP? Model Context Protocol Explained for AI Tooling, Agents, and Interoperability The Model Context Protocol (MCP) is an open, vendor-agnostic standard that connects large language models (LLMs) to…