Every generative AI project starts the same way: an engineer gets a proof of concept working in days.
Most of them never leave that stage. The gap between a working demo and a production system that real
users can depend on is where the majority of GenAI initiatives quietly fail - not because the model is
wrong, but because nobody taught the engineer what production actually requires.
POC TO PRODUCTION closes that gap by following a single generative AI application across its entire
lifecycle, in a single continuous codebase. The book opens with the LangChain mental model and a bare
chat script, then builds outward, chapter by chapter, into a multi-user, memory-aware, RAG-grounded,
guardrail-protected, agentic system - and finally onto scaling the application using Kubernetes on AWS
with CI/CD, observability, and a real feature shipped after go-live. Every chapter runs two tracks: a
Concept Track that explains the idea using exploration-notebook code, and a Code Track that shows the
same idea implemented at production quality, tied to a specific commit in the companion repository.
This is not a tour of techniques. It is a single system, built in front of the reader, with every engineering
decision - and every limitation, and trade-off - left visible instead of edited out. Readers finish the book
having built (or having a clear model for building) an application that handles authentication,
conversational memory, document-grounded retrieval, safety evaluation, adaptive and agentic reasoning,
and a production deployment sized for real internal or pilot usage.