{"product_id":"154869","title":"Generative AI in Action ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDsm0nPVwuivDWs66tnBhY67Bllz.png?v=1765082627\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Generative AI in Action \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/150536946\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLet's learn the knowledge and tools to effectively harness the potential of generative AI and apply them directly to practical applications!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e In controlled environments, deep learning systems consistently outperform humans in reading comprehension, image recognition, and language understanding. \u003cbr\u003eLarge-scale language models (LLMs) can produce similar results in text and image generation and predictive inference.\u003cbr\u003e But outside the lab, in the real world, generative AI can be surprising, but it can also fail miserably.\u003cbr\u003e So how do we get the results we want?\u003cbr\u003e\u003cbr\u003e Generative AI in Action presents real-world examples, insights, and techniques for effectively and safely leveraging large language models (LLMs) and cutting-edge AI technologies.\u003cbr\u003e This book explores practical approaches to applying AI to a variety of human-centric tasks, including marketing, software development, business reporting, and data storytelling. \u003cbr\u003eYou'll also explore cutting-edge design patterns for generative AI applications, learn best practices for prompt engineering, and learn how to address common challenges like hallucinations, high operating costs, and a rapidly changing technology landscape.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[Part 1] The Basics of Generative AI\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 1: Introduction to Generative AI\u003cbr\u003e 1.1 What does this book cover?\u003cbr\u003e 1.2 What is generative AI?\u003cbr\u003e 1.3 What can be created?\u003cbr\u003e __1.3.1 Entity Extraction\u003cbr\u003e __1.3.2 Text Generation\u003cbr\u003e __1.3.3 Image Creation\u003cbr\u003e __1.3.4 Code Generation\u003cbr\u003e __1.3.5 Logical problem solving skills\u003cbr\u003e __1.3.6 Music Creation\u003cbr\u003e __1.3.7 Video Creation\u003cbr\u003e 1.4 Enterprise Use Cases\u003cbr\u003e 1.5 If you do not use generative AI\u003cbr\u003e 1.6 How does generative AI differ from traditional AI?\u003cbr\u003e 1.7 What approach should companies take?\u003cbr\u003e 1.8 Architectural Considerations\u003cbr\u003e 1.9 Process for Introducing Generative AI in Businesses\u003cbr\u003e \u003cbr\u003e▣ Chapter 2: Introduction to Large-Scale Language Models\u003cbr\u003e 2.1 Base Model Overview\u003cbr\u003e 2.2 LLM Overview\u003cbr\u003e 2.3 Transformer Architecture\u003cbr\u003e 2.4 Training Cutoff\u003cbr\u003e 2.5 Types of LLM\u003cbr\u003e 2.6 Small-scale language models\u003cbr\u003e 2.7 Open Source vs.\u003cbr\u003e Commercial LLM\u003cbr\u003e __2.7.1 Commercial LLM\u003cbr\u003e __2.7.2 Open Source LLM\u003cbr\u003e 2.8 Key Concepts of LLM\u003cbr\u003e __2.8.1 prompt\u003cbr\u003e __2.8.2 Token\u003cbr\u003e __2.8.3 Token Calculation\u003cbr\u003e __2.8.4 Embedding\u003cbr\u003e __2.8.5 Model Configuration\u003cbr\u003e __2.8.6 Context Window\u003cbr\u003e __2.8.7 Prompt Engineering\u003cbr\u003e __2.8.8 Model Adaptation\u003cbr\u003e __2.8.9 Emergent Behavior\u003cbr\u003e\u003cbr\u003e ▣ Chapter 3: Working with APIs - Text Generation\u003cbr\u003e 3.1 Model Categories\u003cbr\u003e __3.1.1 Dependencies\u003cbr\u003e __3.1.2 Model query\u003cbr\u003e 3.2 Completed API\u003cbr\u003e __3.2.1 Completed Extension\u003cbr\u003e __3.2.2 Azure Content Safety Filter\u003cbr\u003e __3.2.3 Multiple Completion\u003cbr\u003e __3.2.4 Randomness Control\u003cbr\u003e __3.2.5 Randomness Control Using top_p\u003cbr\u003e 3.3 Advanced Completion API Options\u003cbr\u003e __3.3.1 Streaming Complete\u003cbr\u003e __3.3.2 Factors affecting token probability: logit_bias\u003cbr\u003e __3.3.3 Presence and Frequency Penalties\u003cbr\u003e __3.3.4 Log probability\u003cbr\u003e 3.4 Conversational Completion API\u003cbr\u003e __3.4.1 System Roles  \u003cbr\u003e__3.4.2 Reason for completion\u003cbr\u003e __3.4.3 Conversational Completion API for Non-Chat Scenarios\u003cbr\u003e __3.4.4 Conversation Management\u003cbr\u003e __3.4.5 Best Practices for Token Management\u003cbr\u003e __3.4.6 Additional LLM Providers\u003cbr\u003e\u003cbr\u003e ▣ Chapter 4: From Pixels to Photos - Image Creation\u003cbr\u003e 4.1 Vision Model\u003cbr\u003e __4.1.1 Variational Autoencoder\u003cbr\u003e __4.1.2 Generative Adversarial Networks\u003cbr\u003e __4.1.3 Vision Transformer Model\u003cbr\u003e __4.1.4 Diffusion Model\u003cbr\u003e __4.1.5 Multimodal Model\u003cbr\u003e 4.2 Image generation through stable diffusion\u003cbr\u003e __4.2.1 Dependencies\u003cbr\u003e __4.2.2 Creating an Image\u003cbr\u003e 4.3 Creating images through other providers\u003cbr\u003e __4.3.1 OpenAI DALL·E 3\u003cbr\u003e __4.3.2 Bing Image Creator\u003cbr\u003e __4.3.3 Adobe Firefly\u003cbr\u003e 4.4 Editing and Enhancing Images Using Stable Diffusion\u003cbr\u003e __4.4.1 Creating an Image Using the Image-to-Image API\u003cbr\u003e __4.4.2 Using the Masking API\u003cbr\u003e __4.4.3 Resizing images using the upscale API\u003cbr\u003e __4.4.4 Image Creation Tips\u003cbr\u003e\u003cbr\u003e ▣ Chapter 5: What else can artificial intelligence create?\u003cbr\u003e 5.1 Code Generation  \u003cbr\u003e__5.1.1 Can I trust the code?\u003cbr\u003e __5.1.2 GitHub Copilot\u003cbr\u003e __5.1.3 How Copilot Works\u003cbr\u003e 5.2 Additional Code Related Work\u003cbr\u003e __5.2.1 Code Description\u003cbr\u003e __5.2.2 Test Generation\u003cbr\u003e __5.2.3 Code Reference\u003cbr\u003e __5.2.4 Code Refactoring\u003cbr\u003e 5.3 Other code generation tools\u003cbr\u003e __5.3.1 Amazon CodeWhisperer\u003cbr\u003e __5.3.2 Code Llama\u003cbr\u003e __5.3.3 Tab Nine\u003cbr\u003e __5.3.4 Self-check\u003cbr\u003e __5.3.5 Best Practices for Code Generation\u003cbr\u003e 5.4 Video Creation\u003cbr\u003e 5.5 Audio and Music Creation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] Advanced Techniques and Applications\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 6: Prompt Engineering Guide\u003cbr\u003e 6.1 What is Prompt Engineering?\u003cbr\u003e __6.1.1 Why Prompt Engineering is Needed\u003cbr\u003e 6.2 Fundamentals of Prompt Engineering\u003cbr\u003e 6.3 Learning and Prompting in Context\u003cbr\u003e 6.4 Prompt Engineering Techniques\u003cbr\u003e __6.4.1 System Messages\u003cbr\u003e __6.4.2 Zero-shot learning, few-shot learning, and multi-shot learning\u003cbr\u003e __6.4.3 Use clear syntax\u003cbr\u003e __6.4.4 How to make learning in context work well\u003cbr\u003e __6.4.5 Inference: Chain of Thought  \u003cbr\u003e__6.4.6 Self-consistent sampling\u003cbr\u003e 6.5 Image Prompting\u003cbr\u003e 6.6 Prompt Injection\u003cbr\u003e 6.7 Prompt Engineering Challenges\u003cbr\u003e 6.8 Best Practices\u003cbr\u003e\u003cbr\u003e ▣ Chapter 7: Augmented Search Creation: The Secret Weapon\u003cbr\u003e 7.1 What is RAG?\u003cbr\u003e 7.2 Advantages of RAG\u003cbr\u003e 7.3 RAG Architecture\u003cbr\u003e 7.4 Search Engine System\u003cbr\u003e 7.5 Understanding Vector Databases\u003cbr\u003e __7.5.1 What is a vector index?\u003cbr\u003e __7.5.2 Vector Search\u003cbr\u003e 7.6 RAG Task\u003cbr\u003e 7.7 Solutions to Chunking-Related Challenges\u003cbr\u003e __7.7.1 Chunking Strategy\u003cbr\u003e __7.7.2 Factors Influencing Chunking Strategy\u003cbr\u003e __7.7.3 Handling unknown complexity\u003cbr\u003e __7.7.4 Sentence-level chunking\u003cbr\u003e __7.7.5 Chunking using natural language processing\u003cbr\u003e 7.8 PDF Chunking\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Chatting with Data\u003cbr\u003e 8.1 Benefits of Companies Leveraging Their Own Data\u003cbr\u003e __8.1.1 Pros and Cons of Large Context Windows\u003cbr\u003e __8.1.2 Building a Chat Application Using Data\u003cbr\u003e 8.2 Using a Vector Database\u003cbr\u003e 8.3 Planning for Information Retrieval\u003cbr\u003e 8.4 Data Retrieval\u003cbr\u003e __8.4.1 Finder Pipeline Best Practices  \u003cbr\u003e8.5 Searching with Redis\u003cbr\u003e 8.6 Comprehensive chat implementation based on RAG\u003cbr\u003e 8.7 Using Azure OpenAI on your data\u003cbr\u003e 8.8 Benefits of Integrating Enterprise Data into RAG\u003cbr\u003e\u003cbr\u003e ▣ Chapter 9: Model Customization through Model Adaptation and Fine-Tuning\u003cbr\u003e 9.1 What is model adaptation?\u003cbr\u003e __9.1.1 Basics of Model Adaptation\u003cbr\u003e __9.1.2 Advantages and Challenges in Business\u003cbr\u003e 9.2 When to Fine-Tune LLM\u003cbr\u003e __9.2.1 Key steps in LLM fine-tuning\u003cbr\u003e 9.3 Fine-tuning the OpenAI model\u003cbr\u003e __9.3.1 Preparing the Data Set for Fine-Tuning\u003cbr\u003e __9.3.2 LLM Evaluation\u003cbr\u003e __9.3.3 Fine Tuning\u003cbr\u003e __9.3.4 Fine-tuning training metrics\u003cbr\u003e __9.3.5 Fine-tuning with Azure OpenAI\u003cbr\u003e 9.4 Deploying the Fine-Tuned Model\u003cbr\u003e __9.4.1 Inference: Fine-tuned Model\u003cbr\u003e 9.5 LLM Training\u003cbr\u003e __9.5.1 Pre-training\u003cbr\u003e __9.5.2 Map Fine Tuning\u003cbr\u003e __9.5.3 Compensation Modeling\u003cbr\u003e __9.5.4 Reinforcement Learning\u003cbr\u003e __9.5.5 Direct Policy Optimization\u003cbr\u003e 9.6 Model Adaptation Techniques\u003cbr\u003e __9.6.1 Low-Rank Adaptation\u003cbr\u003e 9.7 RLHF Overview\u003cbr\u003e __9.7.1 RLHF's Challenges\u003cbr\u003e __9.7.2 Extending the RLHF Implementation\u003cbr\u003e \u003cbr\u003e\u003cb\u003e[Part 3] Distribution and Ethical Considerations\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 10: Application Architecture for Generative AI Apps\u003cbr\u003e 10.1 Generative AI: Application Architecture\u003cbr\u003e __10.1.1 Software 2.0\u003cbr\u003e __10.1.2 The Age of Co-Pilots\u003cbr\u003e 10.2 Generative AI: Application Stack\u003cbr\u003e __10.2.1 Integrating the Generative AI Stack\u003cbr\u003e __10.2.2 Generative AI Architecture Principles\u003cbr\u003e __10.2.3 Generative AI Application Architecture: A Detailed View\u003cbr\u003e 10.3 Orchestration Layer\u003cbr\u003e __10.3.1 Benefits of the Orchestration Framework\u003cbr\u003e __10.3.2 Orchestration Framework\u003cbr\u003e __10.3.3 Operations Management\u003cbr\u003e __10.3.4 Prompt Management\u003cbr\u003e 10.4 Grounding Layer\u003cbr\u003e __10.4.1 Data Integration and Preprocessing\u003cbr\u003e __10.4.2 Embedding and Vector Management\u003cbr\u003e 10.5 Model Hierarchy\u003cbr\u003e __10.5.1 Model Ensemble Architecture\u003cbr\u003e __10.5.2 Model Serving\u003cbr\u003e 10.6 Response Filtering\u003cbr\u003e\u003cbr\u003e ▣ Chapter 11: Scaling Up: Best Practices for Production Deployments\u003cbr\u003e 11.1 Challenges of Production Deployment\u003cbr\u003e 11.2 Deployment Options\u003cbr\u003e 11.3 Managed LLM via API\u003cbr\u003e 11.4 Best Practices for Production Deployments  \u003cbr\u003e__11.4.1 Metrics for LLM Inference\u003cbr\u003e __11.4.2 Delay Time\u003cbr\u003e __11.4.3 Scalability\u003cbr\u003e __11.4.4 PAYGO\u003cbr\u003e __11.4.5 Quotas and Rate Limits\u003cbr\u003e __11.4.6 Quota Management\u003cbr\u003e __11.4.7 Observability\u003cbr\u003e __11.4.8 Security and Compliance Considerations\u003cbr\u003e 11.5 Generative AI Operational Considerations\u003cbr\u003e __11.5.1 Reliability and Performance Considerations\u003cbr\u003e __11.5.2 Managed ID\u003cbr\u003e __11.5.3 Caching\u003cbr\u003e 11.6 LLMOps and MLOps\u003cbr\u003e 11.7 Checklist for Production Deployment\u003cbr\u003e\u003cbr\u003e ▣ Chapter 12: Evaluation and Benchmarks\u003cbr\u003e 12.1 LLM Evaluation\u003cbr\u003e 12.2 Existing evaluation indicators\u003cbr\u003e __12.2.1 BLEU\u003cbr\u003e __12.2.2 ROUGE\u003cbr\u003e __12.2.3 BERTScore\u003cbr\u003e __12.2.4 Example of existing indicator evaluation\u003cbr\u003e 12.3 LLM Task-Specific Benchmarks\u003cbr\u003e __12.3.1 G-Eval: A Measurement Approach for NLG Evaluation\u003cbr\u003e __12.3.2 Example of LLM-based evaluation metrics\u003cbr\u003e __12.3.3 HELM\u003cbr\u003e __12.3.4 HEIM\u003cbr\u003e __12.3.5 HellaSWAG\u003cbr\u003e __12.3.6 Understanding Large-Scale Multitask Languages\u003cbr\u003e __12.3.7 Using Azure AI Studio for Evaluation\u003cbr\u003e __12.3.8 DeepEval: An LLM Evaluation Framework\u003cbr\u003e 12.4 New Evaluation Benchmarks\u003cbr\u003e __12.4.1 SWE-bench\u003cbr\u003e __12.4.2 MMMU\u003cbr\u003e __12.4.3 MoCa\u003cbr\u003e __12.4.4 HaluEval\u003cbr\u003e 12.5 Human Rating\u003cbr\u003e \u003cbr\u003e▣ Chapter 13: A Guide to Ethical Generative AI: Principles, Cases, and Pitfalls\u003cbr\u003e 13.1 Generative AI Risks\u003cbr\u003e __13.1.1 LLM Restrictions\u003cbr\u003e __13.1.2 Hallucinations\u003cbr\u003e 13.2 Understanding Generative AI Attacks\u003cbr\u003e __13.2.1 Prompt Injection\u003cbr\u003e __13.2.2 Example of unsafe output processing\u003cbr\u003e __13.2.3 Model Denial of Service Attack\u003cbr\u003e __13.2.4 Data Poisoning and Backdoors\u003cbr\u003e __13.2.5 Sensitive information leak\u003cbr\u003e __13.2.6 Over-reliance\u003cbr\u003e __13.2.7 Model Hijacking\u003cbr\u003e 13.3 Responsible AI Lifecycle\u003cbr\u003e __13.3.1 Identifying Risk Factors\u003cbr\u003e __13.3.2 Measurement and Evaluation of Risk Factors\u003cbr\u003e __13.3.3 Mitigation of hazard factors\u003cbr\u003e __13.3.4 Transparency and Explainability\u003cbr\u003e 13.4 Red Team\u003cbr\u003e __13.4.1 Red Team Example\u003cbr\u003e __13.4.2 Red Team Tools and Techniques\u003cbr\u003e 13.5 Content Safety\u003cbr\u003e __13.5.1 Azure Content Safety\u003cbr\u003e __13.5.2 Google Perspective API\u003cbr\u003e __13.5.3 Content Filter Evaluation\u003cbr\u003e\u003cbr\u003e ▣ Appendix\u003cbr\u003e A: This book's GitHub repository\u003cbr\u003e B: Responsible AI Tools\u003cbr\u003e __B.1 Model Card\u003cbr\u003e __B.2 Transparency Documentation\u003cbr\u003e __B.3 HAX Toolkit\u003cbr\u003e __B.4 Responsible AI Toolbox\u003cbr\u003e __B.5 Learning Interpretation Tool\u003cbr\u003e __B.6 AI Fairness 360  \u003cbr\u003e__B.7 C2PA\u003cbr\u003e C: Azure OpenAI User Guide\u003cbr\u003e __C.1 Create a Microsoft account and prepare an Azure subscription\u003cbr\u003e __C.2 Creating Azure OpenAI Resources\u003cbr\u003e __C.3 Model Deployment\u003cbr\u003e __C.4 Verifying Keys and Endpoints\u003cbr\u003e D: Required Dependency Components Installation Manual\u003cbr\u003e __D.1 Installing Visual Studio Code\u003cbr\u003e __D.2 Installing Conda\u003cbr\u003e __D.3 Installing Python\u003cbr\u003e __D.4 Installing Git\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5495\/MidCate2\/549412043.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ A Practical Overview of Generative AI Applications\u003cbr\u003e ◎ Architectural patterns, integration guides, and best practices for generative AI\u003cbr\u003e ◎ Latest techniques such as RAG, prompt engineering, and multimodality\u003cbr\u003e ◎ Challenges and risks of generative AI, such as hallucinations and jailbreaks\u003cbr\u003e How to Integrate Generative AI into Your Business and IT Strategies \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e August 14, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 540 pages | 188*240*22mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158396305 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e \n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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