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April 18, 2026, 4:49 a.m.
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(Last updated: April 18, 2026, 4:50 a.m.)
| File | Size |
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| Lakshmanan V. Generative Ai Design Patterns. Solutions to Common Challenges 2025.pdf | 16.1 MB |
| Code.zip | 58.2 MB |
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| Uploaded by indexFroggy | Size 209.8 MB | Health [ 23 /3 ] | Added 2023-07-01 |
NOTE
SOURCE: Lakshmanan V. Generative Ai Design Patterns. Solutions to Common Challenges 2025
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COVER
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MEDIAINFO
Textbook in PDF format Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs. This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs. This book bridges that gap by providing 32 battle-tested design patterns that address the recurring problems you’ll encounter when building production-grade GenAI applications. These patterns aren’t theoretical constructs—they codify proven solutions that are often derived from cutting-edge research and refined by practitioners who have successfully deployed GenAI systems at scale. Supervised Machine Learning (ML) involves training a problem-specific model on a large training dataset of example inputs and outputs—but GenAI applications rarely include a training phase. Instead, they commonly use general-purpose foundational models. This book is focused on design patterns for AI applications that are built on top of foundational models, such as Open AI’s GPT, Anthropic’s Claude, Google’s Gemini, or Meta’s Llama. Examples in Python. In this book, we cover the entire AI engineering workflow. After an introduction in Chapter 1, Chapter 2 provides practical patterns for controlling content style and format (including Logits Masking [Pattern 1] and Grammar [Pattern 2]). Chapter 3 and Chapter 4 cover integrating external knowledge through sophisticated retrieval-augmented generation (RAG) implementations, from Basic RAG (Pattern 6) to Deep Search (Pattern 12). Chapter 5 is about enhancing your model’s reasoning capabilities with patterns like Chain of Thought (Pattern 13), Tree of Thoughts (Pattern 14), and Adapter Tuning (Pattern 15). Chapter 6 emphasizes building reliable systems with LLM-as-Judge (Pattern 17), Reflection (Pattern 18), and Prompt Optimization (Pattern 20) patterns. Chapter 7 is about creating agentic systems, including Tool Calling (Pattern 21) and Multiagent Collaboration (Pattern 23). Chapter 8 covers optimizing deployment (including Small Language Model [Pattern 24] and Inference Distribution Testing [Pattern 27]), and Chapter 9 discusses implementing safety guardrails, including Self-Check (Pattern 31) and comprehensive Guardrails (Pattern 32). Design around the limitations of LLMs Ensure that generated content follows a specific style, tone, or format Maximize creativity while balancing different types of risk Build agents that plan, self-correct, take action, and collaborate with other agents Compose patterns into agentic applications for a variety of use cases Who Is This Book For? This book is for software engineers, data scientists, and enterprise architects who are building applications powered by GenAI foundational models. It captures proven solutions you can employ to solve the common challenges that arise when building GenAI applications and agents. Read it to learn how experts in the field are handling challenges such as hallucinations, nondeterministic answers, knowledge cutoffs, and the need to customize a model for your industry or enterprise. The age-old problems of software engineering have new solutions in this realm. For example, ways to meet latency and constrain costs include distillation, speculative decoding, prompt caching, and template generation. Understanding the different patterns in this book requires different levels of background knowledge. For example, Chain of Thought (Pattern 13) requires no more than a knowledge of basic programming, Tool Calling (Pattern 21) requires an understanding of API design, and Dependency Injection (Pattern 19) requires some experience developing large-scale software. However, Content Optimization (Pattern 5) requires familiarity with statistics and ML, and Small Language Model (Pattern 24) requires an understanding of hardware optimization. We expect that 75% of the book can be read and understood by a junior software engineer or a third-year computer science student. The remainder will require specialized knowledge or experience. AI engineering overlaps heavily with software engineering, data engineering, and ML—but in this book, we’ve limited our focus to core AI engineering
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