{"product_id":"140206","title":"LLM Engineering with LLMOps ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYGWruL1MAzGS4nmz2Lw6nYJe9CJZ.png?v=1765078717\" 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 LLM Engineering with LLMOps \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\/145341599\/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\"LLM Engineering Using LLMOps\" is a practical guide to LLMOps that covers the entire process of LLM application development, operation, evaluation, and improvement.\u003cbr\u003e Rather than simply calling models or writing prompts, the focus is on how to solve and manage the problems that are inevitably encountered when applying LLM to products and services.\u003cbr\u003e Additionally, this book is structured to help you learn the flow of LLMOps by developing practical applications, utilizing tools such as LangChain, Streamlet, and Finecon, and implementing functions required for actual operations, such as prompt versioning, performance evaluation automation, and synthetic dataset creation. \u003cbr\u003eIf you read this book from beginning to end, you'll gain insight into the overall operation of the LLM program. Even if you only read the sections you need, you'll discover practical solutions to address the challenges you face immediately.\u003cbr\u003e\n\u003c\/div\u003e\n\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] Basic LLMOps Flow\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 1: LLMOps Flow\u003cbr\u003e 1.1 Background: The iterative process of creating an LLM application\u003cbr\u003e 1.2 Why LLMOps is Needed\u003cbr\u003e 1.3 Differences between MLOps and LLMOps\u003cbr\u003e __1.3.1 Model Complexity and Scale\u003cbr\u003e __1.3.2 Data Management and Processing\u003cbr\u003e __1.3.3 Customization and Optimization\u003cbr\u003e __1.3.4 Monitoring\u003cbr\u003e 1.4 LLMOps Workflow\u003cbr\u003e\u003cbr\u003e ▣ Chapter 2: Background Knowledge for LLM Application Development\u003cbr\u003e 2.1 LLM model selection\u003cbr\u003e __2.1.1 Commercial Closed Model\u003cbr\u003e __2.1.2 Open Source Model\u003cbr\u003e __2.1.3 Differences between closed and open source models  \u003cbr\u003e__2.1.4 Selecting a model based on business requirements\u003cbr\u003e 2.2 Adjusting LLM parameters according to application type\u003cbr\u003e 2.3 Elements of the prompt\u003cbr\u003e __2.3.1 Components of the prompt\u003cbr\u003e __2.3.2 Prompt Role\u003cbr\u003e __2.3.3 Prompt templating\u003cbr\u003e\u003cbr\u003e ▣ Chapter 3: Developing a Practical Customer Inquiry Classification Application\u003cbr\u003e 3.1 Customer Inquiry Classification Application Overview\u003cbr\u003e 3.2 Language Model Selection\u003cbr\u003e __3.2.1 Antropic's Messages API\u003cbr\u003e __3.2.2 Open-source model access using Olama\u003cbr\u003e 3.3 Langchain Overview\u003cbr\u003e __3.3.1 Why use Langchain?\u003cbr\u003e __3.3.2 Installing Langchain and Building Example Applications\u003cbr\u003e 3.4 Langchain Basics\u003cbr\u003e __3.4.1 Prompt Template\u003cbr\u003e __3.4.2 Chat Model\u003cbr\u003e __3.4.3 Output Parser\u003cbr\u003e __3.4.4 LCEL\u003cbr\u003e 3.5 Developing a Practical Application Using LangChain\u003cbr\u003e __3.5.1 Model Definition\u003cbr\u003e __3.5.2 Defining the Output Parser\u003cbr\u003e __3.5.3 Application Chain Development\u003cbr\u003e\u003cbr\u003e ▣ Chapter 4: LLMOps Tool Development\u003cbr\u003e 4.1 The Need for LLMOps Tools\u003cbr\u003e __4.1.1 Prompt Versioning\u003cbr\u003e __4.1.2 Managing Datasets for Evaluation  \u003cbr\u003e__4.1.3 Performance Evaluation Criteria and Methods\u003cbr\u003e 4.2 Essential features and components of the LLMOps tool\u003cbr\u003e __4.2.1 Test function\u003cbr\u003e __4.2.2 Prompt Management and Versioning Features\u003cbr\u003e __4.2.3 Evaluation\u003cbr\u003e __4.2.4 Dataset Management Function\u003cbr\u003e 4.3 Configuring the Development Environment\u003cbr\u003e __4.3.1 Streamlet for creating easy Python-based web apps\u003cbr\u003e __4.3.2 SQLite for storing data\u003cbr\u003e 4.4 LLM Test Function\u003cbr\u003e __4.4.1 Why Testing UI is Needed\u003cbr\u003e __4.4.2 Implementing a model management class supported by the test function\u003cbr\u003e __4.4.3 Creating a test menu using Streamlet\u003cbr\u003e 4.5 Prompt Management and Versioning\u003cbr\u003e __4.5.1 Why Prompt Management is Needed\u003cbr\u003e __4.5.2 Table design for prompt management\u003cbr\u003e __4.5.3 Implementing a class for prompt management\u003cbr\u003e __4.5.4 Streamlet Test Menu Improvements: Support for Saving Prompts and Versioning\u003cbr\u003e __4.5.5 Implementing a class to manage specific prompt templates\u003cbr\u003e 4.6 Evaluation Criteria\u003cbr\u003e __4.6.1 Various evaluation indicators  \u003cbr\u003e__4.6.2 Implementing an evaluator class that generates evaluation metrics\u003cbr\u003e __4.6.3 Creating and Using Evaluators\u003cbr\u003e 4.7 Dataset\u003cbr\u003e __4.7.1 Table design for dataset management\u003cbr\u003e __4.7.2 Implementing the dataset storage class\u003cbr\u003e __4.7.3 Implementing a class for managing a single dataset\u003cbr\u003e __4.7.4 Creating a dataset management menu using Streamlet\u003cbr\u003e 4.8 Dataset Evaluation\u003cbr\u003e __4.8.1 Table design for dataset evaluation\u003cbr\u003e __4.8.2 Implementing the dataset evaluation class\u003cbr\u003e __4.8.3 Creating an evaluation execution menu using Streamlet\u003cbr\u003e __4.8.4 Creating a menu to view evaluation results using Streamlet\u003cbr\u003e 4.9 LLMOps Menu Configuration\u003cbr\u003e\u003cbr\u003e ▣ Chapter 5: Managing LLM Applications Using LLMOps Tools\u003cbr\u003e 5.1 Design and create a draft prompt\u003cbr\u003e 5.2 Building the Dataset\u003cbr\u003e 5.3 Evaluation Progress\u003cbr\u003e 5.4 Added a new version of the prompt\u003cbr\u003e 5.5 Compare evaluation metrics by version to make a decision.\u003cbr\u003e 5.6 Answer the questions based on the evaluation results\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] LLMOps Flow for RAG\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e▣ Chapter 6: Practical RAG-Based Insurance Chatbot Application\u003cbr\u003e 6.1 Overview of the Insurance Inquiry Chatbot Application\u003cbr\u003e 6.2 General Augmented Search Generation (RAG) Workflow\u003cbr\u003e __6.2.1 Document Indexing Process\u003cbr\u003e __6.2.2 Answer Generation Process\u003cbr\u003e __6.2.3 Further RAG Paradigm\u003cbr\u003e 6.3 Using the Vector Database Pinecone\u003cbr\u003e __6.3.1 Practice: Finding Similar Documents Using Pinecone\u003cbr\u003e 6.4 Reading PDF files and indexing them into a vector database\u003cbr\u003e __6.4.1 Document Chunking\u003cbr\u003e __6.4.2 Document Chunk Vectorization\u003cbr\u003e __6.4.3 Index\u003cbr\u003e 6.5 Find documents most similar to the entered question\u003cbr\u003e __6.5.1 Searching for candidate documents using the embedding model (10 documents)\u003cbr\u003e __6.5.2 Final filtering with re-ranking model (3)\u003cbr\u003e 6.6 Developing a Practice Application Chain\u003cbr\u003e __6.6.1 Search: Find similar documents\u003cbr\u003e __6.6.2 Generate: Generate answers based on documents\u003cbr\u003e\u003cbr\u003e ▣ Chapter 7: Development of LLMOps Tools for RAG\u003cbr\u003e 7.1 Tool functions for the RAG system\u003cbr\u003e 7.2 RAG Evaluator Implementation\u003cbr\u003e __7.2.1 Understanding RAG Evaluation Metrics\u003cbr\u003e __7.2.2 Lagas supporting RAG evaluation metrics  \u003cbr\u003e__7.2.3 Implementing an evaluator for RAG\u003cbr\u003e __7.2.4 Tool Integration: Dynamic Evaluator Support\u003cbr\u003e __7.2.5 Tool Integration: RAG Evaluation Support\u003cbr\u003e __7.2.6 Added Streamlet Evaluation menu function\u003cbr\u003e 7.3 Synthetic dataset creation function\u003cbr\u003e __7.3.1 RAG Question Types\u003cbr\u003e __7.3.2 Knowledge Graph-Based Test Set Generation Pipeline\u003cbr\u003e __7.3.3 Practice: Generating a Synthetic Test Set Based on PDF Documents\u003cbr\u003e __7.3.4 Implementing a synthetic dataset class based on PDF documents\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Managing LLM Applications Using LLMOps Tools\u003cbr\u003e 8.1 Creating a Prompt\u003cbr\u003e 8.2 Creating and Saving Synthetic Datasets\u003cbr\u003e 8.3 Evaluation Progress\u003cbr\u003e 8.4 Analysis of Evaluation Results\u003cbr\u003e __8.4.1 Token Usage and Latency Analysis\u003cbr\u003e __8.4.2 Analysis of context precision and reliability metrics\u003cbr\u003e 8.5 Answer questions based on evaluation results\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3] Continuous Improvement\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ▣ Chapter 9: Ongoing Management of LLM Applications\u003cbr\u003e 9.1 Monitoring\u003cbr\u003e __9.1.1 Tracking Model Usage\u003cbr\u003e __9.1.2 Collecting Performance Metrics\u003cbr\u003e __9.1.3 Detecting Prompt Injection Attacks  \u003cbr\u003e9.2 Resource Management: Cost Reduction and Model Lightness\u003cbr\u003e __9.2.1 Caching\u003cbr\u003e __9.2.2 Model Lightweighting: Quantization\u003cbr\u003e __9.2.3 Model Lightening: Knowledge Distillation\u003cbr\u003e 9.3 Market Direction Driven by Deep Seek\u003cbr\u003e __9.3.1 Development of low-cost, high-performance AI models\u003cbr\u003e __9.3.2 Open Source Strategy\u003cbr\u003e __9.3.3 Introduction of efficient learning techniques\u003cbr\u003e\u003cbr\u003e ▣ Chapter 10: Continuous Improvement of LLMOps Tools\u003cbr\u003e 10.1 Chaining, Agent Support, and Monitoring\u003cbr\u003e __10.1.1 Chaining\u003cbr\u003e __10.1.2 Agent\u003cbr\u003e 10.2 Model Deployment Process Support\u003cbr\u003e __10.2.1 Model Training\u003cbr\u003e __10.2.2 Model Serving\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\/TopCate5265\/MidCate1\/526403910.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 ◎ Understanding the LLMOps flow for iterative development of LLM applications\u003cbr\u003e ◎ Selection criteria and prompt components for closed\/open source LLM models\u003cbr\u003e ◎ Development of a customer inquiry classification application using Langchain\u003cbr\u003e ◎ Development of an LLMOps tool capable of prompt version management, testing, and evaluation. \u003cbr\u003e◎ Streamlet-based prompt\/dataset\/metric management UI configuration\u003cbr\u003e ◎ Implementation of a RAG-based chatbot using Finecon and document chunking\u003cbr\u003e ◎ Creation of a synthetic dataset and a Lagas evaluator for measuring RAG system performance\u003cbr\u003e ◎ Integration of continuous improvement strategies and tools, such as caching, model lightweighting, and monitoring. \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 April 24, 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 328 pages | 175*235*14mm\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 9791158396022 \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\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893405220906,"sku":"140206","price":38.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/95577400ca83f180b7f3936a21ef45b5.jpg?v=1765400603","url":"https:\/\/librairie.coreenne.fr\/en\/products\/140206","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}