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    Home » Computers & Internet » Generative AI Application Integration Patterns: Integrate large language models into your applications
    Computers & Internet

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

    TeamBy TeamAugust 5, 2025No Comments4 Mins Read2 Views
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    1754374987 715mTlcHexL. SL1360 Generative AI Application Integration Patterns: Integrate large language models into your applications Edu Expertise Hub Generative AI
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    Price: $54.99 - $49.99
    (as of Aug 05, 2025 06:23:10 UTC – Details)

    amazon buy now button 300x148 1 Generative AI Application Integration Patterns: Integrate large language models into your applications Edu Expertise Hub Generative AI

    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 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 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 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

    Table of ContentsIntroduction 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

    From the brand

    Brand story Packt booksBrand story Packt books

    See more at our store:

    8c1d2060 23c1 4083 9193 c9c650e56960. CR0,0,315,145 PT0 SX315 V1 Generative AI Application Integration Patterns: Integrate large language models into your applications Edu Expertise Hub Generative AI8c1d2060 23c1 4083 9193 c9c650e56960. CR0,0,315,145 PT0 SX315 V1 Generative AI Application Integration Patterns: Integrate large language models into your applications Edu Expertise Hub Generative AI

    Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

    Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

    We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

    Publisher ‏ : ‎ Packt Publishing
    Publication date ‏ : ‎ September 5, 2024
    Language ‏ : ‎ English
    Print length ‏ : ‎ 218 pages
    ISBN-10 ‏ : ‎ 1835887600
    ISBN-13 ‏ : ‎ 978-1835887608
    Item Weight ‏ : ‎ 13.4 ounces
    Dimensions ‏ : ‎ 7.5 x 0.5 x 9.25 inches
    Best Sellers Rank: #968,767 in Books (See Top 100 in Books) #94 in Project Management Software Books #399 in Natural Language Processing (Books) #458 in Computer Neural Networks
    Customer Reviews: 4.8 4.8 out of 5 stars 10 ratings var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });

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