AI Programming with Python for Developers: Build Real-World AI Applications, LLM Solutions, RAG Pipelines, and Autonomous Agents

Description

AI development is moving beyond experiments. It's becoming real software engineering.The question isn't whether you can call an LLM API—anyone can do that. The real challenge is building AI applications that actually work in production: reliable, testable, secure, and scalable.This book takes you from your first model call to a deployed, production-ready AI system.Henry Bill, an experienced AI engineering practitioner, guides you beyond the hype and into the code. You won't just learn theory—you'll build real-world applications step by step, gradually combining components into systems that handle real users, data, and infrastructure constraints.What You Will BuildLLM-Powered Applications with structured outputs, streaming, and proper validationTool-Using AI Assistants with controlled, secure function callingProduction-Ready RAG Systems with embeddings, vector search, hybrid retrieval, and rerankingAutonomous AI Agents with state management, multi-step workflows, and controlled executionMCP-Connected Systems using the Model Context ProtocolLocal and Private AI Applications with Ollama and vLLMTested, Secured, and Deployed systems with monitoring and production-grade infrastructureWhy This Book Is DifferentMost AI books teach you how to use one framework or provider. This book teaches the underlying patterns that remain useful as models, APIs, and libraries change.You'll learn to build applications that depend on abstractions rather than provider-specific calls. This separation lets you move between hosted models, local models, and self-hosted inference without rewriting your application.What You Will LearnDesign effective model requests and manage conversation stateCreate structured outputs your Python code can actually useBuild tools that give models controlled access to your systemsImplement RAG pipelines with metadata filtering and hybrid searchConstruct autonomous agents with state, memory, and multi-step workflowsConnect AI applications to external capabilities through MCPRun local models with Ollama and vLLM for private deploymentsTest, evaluate, trace, secure, and optimize AI applicationsDeploy containerized AI backends with FastAPI, Docker, and monitoringWho This Book Is ForPython Developers adding AI capabilities to their applicationsAI Engineers moving from experimentation to productionSoftware Architects designing AI components within larger platformsTechnical Team Leads building practical, maintainable AI solutionsWhat You Need to KnowYou should be comfortable with Python and basic software engineering. You don't need to be an AI researcher—the book teaches everything else.Ready to build AI applications that actually work?This book gives you everything you need—from environment setup to production deployment. No fluff. No theory that doesn't translate to code. Just practical, engineering-focused guidance.Scroll up, click "Buy Now," and start building today.

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