From Prompt to Production

Why do 80% of enterprise generative AI pilots fail?Recent industry reports converge on a sobering truth: four out of five LLM prototypes quietly die before reaching production. From Prompt to Production is the definitive field manual for bridging that gap and successfully deploying resilient, production-ready AI systems.One Running Application, Twelve Lifecycle PhasesUnlike books that offer disconnected code snippets, this guide follows a single realistic enterprise application-Helix Insight-across an eighteen-month lifecycle. You will witness firsthand how early scoping decisions ripple into evaluation design, how rollout strategies impact reliability, and how to handle real-world incidents.What You Will Master:Scoping & Discovery: Define defensible LLM problems and write robust PRDs.Prototyping & Hardening: Move from fragile Python scripts to secure, bounded architectures.Eval-Driven Development: Build a four-layer evaluation pyramid and implement LLM-as-judge.Advanced RAG & Tool Use: Design data pipelines and secure agentic workflows with loop limits.Observability & Reliability: Track LLM-specific metrics and manage production incidents.Whether you are an AI engineer, a product manager, or a solution architect, this book provides the complete narrative map you need to industrialize generative AI safely and cost-effectively.

Why do 80% of enterprise generative AI pilots fail?Recent industry reports converge on a sobering truth: four out of five LLM prototypes quietly die before reaching production. From Prompt to Production is the definitive field manual for bridging that gap and successfully deploying resilient, production-ready AI systems.One Running Application, Twelve Lifecycle PhasesUnlike books that offer disconnected code snippets, this guide follows a single realistic enterprise application-Helix Insight-across an eighteen-month lifecycle. You will witness firsthand how early scoping decisions ripple into evaluation design, how rollout strategies impact reliability, and how to handle real-world incidents.What You Will Master:Scoping & Discovery: Define defensible LLM problems and write robust PRDs.Prototyping & Hardening: Move from fragile Python scripts to secure, bounded architectures.Eval-Driven Development: Build a four-layer evaluation pyramid and implement LLM-as-judge.Advanced RAG & Tool Use: Design data pipelines and secure agentic workflows with loop limits.Observability & Reliability: Track LLM-specific metrics and manage production incidents.Whether you are an AI engineer, a product manager, or a solution architect, this book provides the complete narrative map you need to industrialize generative AI safely and cost-effectively.