Generative AI Application Integration Pattes:Integrate large language models into your applications

by:Juan Pablo Bustos (Author),Luis Lopez Soria(Author)

Publisher:Packt Publishing

Publication Date:2024/9/5

Language:English

Print Length:218 pages

ISBN-10:1835887600

ISBN-13:9781835887615

Book Description

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 haess these technologies through proven design pattes.We then delve into the practical applications of GenAI, identifying common use cases and applying design pattes 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 lea 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 goveance as crucial design pattes.What you will leaConcepts of GenAI:pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI:entry points, prompt pre-processing, inference, post-processing, and presentationPattes for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: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 include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design pattesProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights for experienced professionalsTable of ContentsIntroduction to Generative AI Design PattesIdentifying Generative AI Use CasesDesigning Pattes for Interacting with Generative AIGenerative AI Batch & Real-time Integration PattesIntegration Patte:Batch Metadata ExtractionIntegration Patte:Batch SummarizationIntegration Patte:Real-Time Intent ClassificationIntegration Patte:Real-Time Retrieval Augmented GenerationOperationalizing Generative AI Integration PattesEmbedding Responsible AI into your GenAI Applications

About the Author

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 haess these technologies through proven design pattes.We then delve into the practical applications of GenAI, identifying common use cases and applying design pattes 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 lea 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 goveance as crucial design pattes.What you will leaConcepts of GenAI:pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI:entry points, prompt pre-processing, inference, post-processing, and presentationPattes for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: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 include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design pattesProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights for experienced professionalsTable of ContentsIntroduction to Generative AI Design PattesIdentifying Generative AI Use CasesDesigning Pattes for Interacting with Generative AIGenerative AI Batch & Real-time Integration PattesIntegration Patte:Batch Metadata ExtractionIntegration Patte:Batch SummarizationIntegration Patte:Real-Time Intent ClassificationIntegration Patte:Real-Time Retrieval Augmented GenerationOperationalizing Generative AI Integration PattesEmbedding Responsible AI into your GenAI Applications

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