Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications
Design around the limitations of LLMs, such as hallucination and nondeterminism
Force LLMs to generate text that follows a specific style or grammar
Maximize creativity while balancing different types of risk
Extend the capability of an LLM beyond just content creation
Use patterns together to solve a variety of different use cases
Explore the core architecture of AI agents: tools, memory, and planning
Understand reasoning LLMs, multimodal models, and multi-agent collaboration
Learn advanced methods, including distillation, quantization, and reinforcement learning
Evaluate real-world applications, strengths, and limitations of AI agents
Sometimes your LLMs return brilliant responses. Other times, not so much. Do you know why? Prompt Engineering in Practice shows you how to move from accidental AI results to reliable, production-grade systems you can deploy with confidence. Written by AI veterans Richard Davies and Rafael Fischer, this book introduces a unique approach: treat prompts as engineered, self-contained interfaces that you can compose, evaluate, and refine. This shift reframes model interactions as a strict software design discipline rather than a series of fuzzy trial-and-error exercises.