"Ray Serve for LLM Apps: Scalable APIs, Batching, and Async Tool Pipelines" Modern LLM applications rarely fail because a model cannot generate text; they fail because the serving layer collapses under real traffic, tool latency, streaming demands, and constant API evolution. This book is written for experienced Python, backend, and platform engineers who need to build serious LLM systems on Ray Serve. It assumes readers want architectural clarity, operational depth, and production-grade patterns rather than introductory examples or lightweight demos. Across the book, readers learn how to design robust HTTP and FastAPI ingress layers, compose deployments with the modern `DeploymentHandle` model, and build non-blocking execution graphs for retrieval, generation, and tool use. It explains dynamic batching as a throughput lever, shows when batching conflicts with interactivity, and develops practical strategies for streaming responses, partial results, timeout handling, autoscaling, and distributed inference. The result is a disciplined framework for turning LLM workflows into scalable, evolvable, and observable services. A key strength of the book is its focus on the boundary between public API contracts and internal execution topology. It covers both core Ray Serve primitives and the higher-level Ray Serve LLM stack, helping readers decide when to use generic deployments and when specialized abstractions pay off. The treatment is version-aware, operationally grounded, and aimed at teams running production systems where latency, utilization, and reliability must be tuned t