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, depl...
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Birmingham
Packt Publishing
2024
Packt Publishing, Limited Packt Publishing Limited |
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| ISBN: | 9781835887608, 1835887600 |
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| Abstract | 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 Features
Get 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 applications
Book Description
Explore 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 learn
Concepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: 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 use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI models
Who this book is for
This 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 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 |
|---|---|
| AbstractList | <![CDATA[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 GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: 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 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 patternsProfessionals 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 professionals]]> 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 Features Get 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 applications Book Description Explore 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 learn Concepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: 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 use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI models Who this book is for This 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 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 Explore 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. |
| Author | Soria, Luis Lopez Bustos, Juan Pablo |
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| ContentType | eBook |
| Contributor | Arsanjani, Dr. Ali |
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| Copyright | 2024 Packt Publishing 2024 |
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| Snippet | Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into... Explore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation... <![CDATA[Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language... |
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| SubjectTerms | Application software-Development Artificial intelligence COM081000 COMPUTERS / Desktop Applications / Project Management Software COMPUTERS / Natural Language Processing COMPUTERS / Neural Networks Computing and Processing Corpora (Linguistics) General References Generative programming (Computer science) Software Engineering |
| Subtitle | Integrate large language models into your applications |
| TableOfContents | Table of Contents
Introduction to Generative AI Design PatternsIdentifying Generative AI Use CasesDesigning Patterns for Interacting with Generative AIGenerative AI Batch & Real-time Integration PatternsIntegration Pattern: Batch Metadata ExtractionIntegration Pattern: Batch SummarizationIntegration Pattern: Real-Time Intent ClassificationIntegration Pattern: Real-Time Retrieval Augmented GenerationOperationalizing Generative AI Integration PatternsEmbedding Responsible AI into your GenAI Applications Title Page Preface Table of Contents 1. Introduction to Generative AI Patterns 2. Identifying Generative AI Use Cases 3. Designing Patterns for Interacting with Generative AI 4. Generative AI Batch and Real-Time Integration Patterns 5. Integration Pattern: Batch Metadata Extraction 6. Integration Pattern: Batch Summarization 7. Integration Pattern: Real-Time Intent Classification 8. Integration Pattern: Real-Time Retrieval Augmented Generation 9. Operationalizing Generative AI Integration Patterns 10. Embedding Responsible AI into Your GenAI Applications Index Cover -- Copyright -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Generative AI Patterns -- From AI predictions to generative AI -- Predictive AI vs generative AI use case ideation -- A change in the paradigm -- Predictive AI use case development - simplified lifecycle -- Generative AI use case development - simplified lifecycle -- General generative AI concepts -- Generative AI model architectures -- Techniques available to optimize foundational models -- Techniques to augment your foundational model responses -- Constant evolution across the generative AI space -- Introducing generative AI integration patterns -- Summary -- Chapter 2: Identifying Generative AI Use Cases -- When to consider generative AI -- Realizing business value -- Identifying Generative AI use cases -- Potential business-focused use cases -- Generative AI deployment and hosting options -- Summary -- Chapter 3: Designing Patterns for Interacting with Generative AI -- Defining an integration framework -- Entry point -- Prompt pre-processing -- Inference -- Results post-processing -- Selecting from amongst multiple outputs -- Refining generated outputs -- Results presentation -- Logging -- Summary -- Chapter 4: Generative AI Batch and Real-Time Integration Patterns -- Batch and real-time integration patterns -- Different pipeline architectures -- Application integration patterns in the integration framework -- Entry point -- Prompt preprocessing -- Inference -- Result post-processing -- Result presentation -- Use case example - search enhanced by GenAI -- Batch integration - document ingestion -- Real-time integration - search -- Summary -- Chapter 5: Integration Pattern: Batch Metadata Extraction -- Use case definition -- Architecture -- Entry point -- Prompt pre-processing -- Inference -- Result post-processing -- Result presentation Summary -- Chapter 6: Integration Pattern: Batch Summarization -- Use case definition -- Architecture -- Entry point -- Prompt preprocessing -- Inference -- Result post-processing -- Result presentation -- Summary -- Chapter 7: Integration Pattern: Real-Time Intent Classification -- Use case definition -- Architecture -- Entry point -- Prompt pre-processing -- Inference -- Result post-processing -- Result presentation -- Logging and monitoring -- Summary -- Chapter 8: Integration Pattern: Real-Time Retrieval Augmented Generation -- Use case definition -- Architecture -- Entry point -- Prompt preprocessing -- Inference -- Result post-processing -- Result presentation -- Use case demo -- The Gradio app -- Summary -- Chapter 9: Operationalizing Generative AI Integration Patterns -- Operationalization framework -- Data layer -- A real-world example: Part 1 -- Training layer -- A real-world example: Part 2 -- Inference layer -- A real-world example: Part 3 -- Operations layer -- CI/CD and MLOps -- Monitoring and observability -- Evaluation and monitoring -- Alerting -- Distributed tracing -- Logging -- Cost optimization -- Summary -- Chapter 10: Embedding Responsible AI into Your GenAI Applications -- Introduction to responsible AI -- Fairness in GenAI applications -- Interpretability and explainability -- Privacy and data protection -- Safety and security in GenAI systems -- Google's approach to responsible AI -- Google's Secure AI Framework (SAIF) -- Google's Red Teaming approach -- Anthropic's approach to responsible AI -- Summary -- Other Books You May Enjoy -- Index Generative AI Application Integration Patterns: Integrate large language models into your applications |
| Title | Generative AI Application Integration Patterns |
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