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Generative AI Design Patterns: Proven Architectures for LLM Applications, Agents, RAG Systems, and AI Workflows
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Publisher: Independently Published
Published: Jan 2 2026
Pages: 290
Weight: 0.86
Height: 0.61 Width: 6.00 Depth: 9.00
Language: English
Generative AI has moved rapidly from experimentation to production-but building reliable, scalable, and maintainable AI systems remains a major challenge. Many teams struggle with unpredictable model outputs, fragile prompts, unreliable retrieval pipelines, uncontrolled agent behavior, rising costs, and complex orchestration across tools and data. The problem is no longer access to powerful models-it is system design.
Generative AI Design Patterns provides a practical, architecture-focused guide to building production-ready systems powered by large language models (LLMs). Instead of focusing on a single model, framework, or vendor, this book distills proven design patterns that capture best practices for designing real-world generative AI solutions.
Grounded in real production scenarios, the book emphasizes system-level thinking-helping engineers and architects design AI systems that are easier to reason about, operate, scale, and evolve over time.
In this book, you will learn how to:
Design robust prompt and interaction patterns for consistent, controllable outputs
Build retrieval-augmented generation (RAG) systems that reduce hallucinations and ground responses in trusted data
Architect agentic and multi-agent systems for planning, tool use, and autonomous execution
Orchestrate AI workflows and pipelines across services, tools, and human approvals
Apply evaluation, reliability, and cost-control patterns for production environments
Embed security, governance, and compliance into generative AI architectures
Combine patterns into end-to-end reference architectures for LLM applications and platforms
Who This Book Is ForThis book is ideal for:
Software engineers building LLM-powered applications
Machine learning and platform engineers designing AI systems
A basic understanding of software development and APIs is assumed. No deep background in machine learning is required-the focus is on engineering judgment and architectural design, not model training.
Architects responsible for production and enterprise AI solutions
Technical leaders scaling generative AI initiatives
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