Enterprise Playbook

Enterprise RAG Architecture: Internal Knowledge Brain Answer-First Summary: Enterprise RAG architectures replace naive text chunking with multi-stage data pipelines combining layout-aware global scanning, hybrid dense-sparse vector search, and cross-encoder context reranking. This architecture eliminates table slicing hallucinations, enforces metadata access controls, and cuts retrieval prompt token overhead by 70% while maintaining sub-400ms end-to-end query latency. 90% of RAG (Retrieval-Augmented Generation) tutorials online are simple toy examples: Write 10 lines of Python, read a PDF file, perform naive chunking, stuff it into a Vector Database, and then run a Q&A. ...

AI Observability & Evals: Production Monitoring Guide Answer-First Summary: AI Observability applies Site Reliability Engineering (SRE) principles to generative AI systems through OpenTelemetry gen_ai semantic conventions, distributed prompt tracing, and continuous evaluation pipelines. This framework detects silent model drift, monitors LLM API token expenses, and reduces failure detection time from weeks to under five minutes. Many engineers in the current market can build an AI App in a weekend. But those who know how to operate an AI system in production (AI Platform Operations) can be counted on one hand. ...