Production Evals & Guardrails: LLM-as-a-Judge Scale

📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Familiarity with distributed tracing and observability metrics established in Part 9 — Agentic Observability: OpenTelemetry. Part 10 — Production Evals & CI/CD Guardrails: LLM-as-a-Judge at Scale In traditional software development, continuous integration (CI) relies on deterministic unit and integration tests: a function either returns the exact expected struct or it breaks the build. In enterprise GenAI and RAG pipelines, responses are inherently non-deterministic. A subtle system prompt tweak, an updated embedding model, or a re-indexed chunk size can silently introduce catastrophic hallucinations or drop critical context facts without triggering a single compilation error. ...

Part 6: AI Observability, OpenTelemetry GenAI & Continuous Evaluation

Answer-first: Traditional Application Performance Monitoring (APM) tools fail to capture generative AI failure modes because an HTTP 200 response can still contain complete factual hallucinations, toxic responses, or $50.00 runaway token loops. Modern AI Observability implements OpenTelemetry GenAI Semantic Conventions v1.30+, correlating distributed multi-agent traces with token spend, Time-to-First-Token (TTFT), and automated LLM-as-a-Judge evaluation pipelines (Ragas / Phoenix). 📖 Bản tiếng Việt (Vietnamese Edition) | ← Series Hub | Next Chapter: Part 7: AI Security Engineering & DevSecOps → ...