Build Intelligent Apps with RAG and AI Agents

Build a user-facing AI product from a validated problem to production, combining RAG, agents, deployment, user acquisition, and iteration in one cohesive project Key Features Build one cohesive RAG and agent product from a validated idea to production Use principles-first guidance to make sound choices across fast-changing AI tools Go beyond the prototype with deployment, user acquisition, evaluation, and iteration Book DescriptionMany AI projects work as demos but never become products that people use. This book focuses on the full product lifecycle: choosing a worthwhile problem, defining a scoped solution, building the application, deploying it, finding users, and improving it with real feedback. You begin by validating demand, mapping the user journey, and selecting an architecture that fits the problem. From there, you build a RAG-based assistant in Python, add tool use and function calling for agent workflows, and make informed choices across LLMs, vector stores, and frontend options. The project then moves into production with cloud infrastructure, CI/CD, access controls, and cost guardrails. The final chapters address acquisition, pricing, structured logging, LLM evaluation, prompt versioning, user behavior, and post-launch paths. By the end, you will understand how the RAG and agent stack fits into a complete AI product and how to move from an idea to a deployed, user-facing application you can continue to improve. What you will learn Validate an AI product problem before committing to a build Design a scoped product journey and choose the right architecture Build a RAG assistant with Python and an LLM API Add tool use and function calling to create agent workflows Deploy an AI application with CI/CD and cost controls Acquire early users and gather structured…