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Generative AI Application Integration Patterns: Integrate large language models into your applications
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.
Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.
With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.
We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.
Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
What you will learnConcepts of pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most
Developer engineers with foundational tech knowledge
Software architects seeking best practices and design patterns
Professionals using ML for data science, research, etc., who want a deeper understanding of Generative AI
Technical product managers with a software development background
This concise focus ensures practical, actionable insights for experienced professionals
Table of ContentsIntroduction to Generative AI Design PatternsIdentifying Generative AI Use CasesDesigning Patterns for Interacting with Generative AIGenerative AI Batch & Real-time Integration PatternsIntegration Batch Metadata ExtractionIntegration Batch Sum
Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.
With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.
We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.
Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
What you will learnConcepts of pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most
Developer engineers with foundational tech knowledge
Software architects seeking best practices and design patterns
Professionals using ML for data science, research, etc., who want a deeper understanding of Generative AI
Technical product managers with a software development background
This concise focus ensures practical, actionable insights for experienced professionals
Table of ContentsIntroduction to Generative AI Design PatternsIdentifying Generative AI Use CasesDesigning Patterns for Interacting with Generative AIGenerative AI Batch & Real-time Integration PatternsIntegration Batch Metadata ExtractionIntegration Batch Sum
320 pages, Kindle Edition
Published September 5, 2024
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