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AI Programming with Python for Developers: Build Real-World AI Applications, LLM Solutions, RAG Pipelines, and Autonomous Agents
Paperback
Publisher: Independently Published
Published: Aug 18 2026
Pages: 364
Weight: 1.39
Height: 0.75 Width: 7.00 Depth: 10.00
Language: English
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 validation
Tool-Using AI Assistants with controlled, secure function calling
Production-Ready RAG Systems with embeddings, vector search, hybrid retrieval, and reranking
Autonomous AI Agents with state management, multi-step workflows, and controlled execution
MCP-Connected Systems using the Model Context Protocol
Local and Private AI Applications with Ollama and vLLM
Tested, Secured, and Deployed systems with monitoring and production-grade infrastructure
Most 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 state
Create structured outputs your Python code can actually use
Build tools that give models controlled access to your systems
Implement RAG pipelines with metadata filtering and hybrid search
Construct autonomous agents with state, memory, and multi-step workflows
Connect AI applications to external capabilities through MCP
Run local models with Ollama and vLLM for private deployments
Test, evaluate, trace, secure, and optimize AI applications
Deploy containerized AI backends with FastAPI, Docker, and monitoring
Python Developers adding AI capabilities to their applications
AI Engineers moving from experimentation to production
Software Architects designing AI components within larger platforms
Technical Team Leads building practical, maintainable AI solutions
You 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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