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Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Debugging and Optimizing RAG Pipelines: A Practical Guide for AI DevelopersThis hands-on guide explores the challenges and solutions of building high-performance Retrieval-Augmented Generation (RAG) pipelines. As AI-powered applications become more complex, understanding how to monitor, debug, and fine-tune RAG systems is essential. This book provides a clear and practical roadmap for developers working with large language models, search engines, and generation components to ensure reliability, accuracy, and efficiency in production-grade AI systems.From real-time monitoring to error tracing and optimization techniques, this book walks you through every stage of a RAG pipeline. Whether you're troubleshooting hallucinations, improving retrieval quality, or scaling a system for enterprise use, you'll find actionable guidance and ready-to-use code examples that save time and reduce friction. Debugging and Optimizing RAG Pipelines goes beyond just theory. It addresses real-world challenges that AI developers face when building and deploying RAG systems. You'll learn to identify issues early, implement observability tools, reduce latency, eliminate hallucinations, and continuously improve system performance. Each chapter includes practical tips, hands-on examples, and expert insights designed to help you create RAG pipelines that are robust, scalable, and easy to maintain.Key Features of This BookReal-world debugging workflows for complex RAG systemsBest practices for prompt design, logging, and feedback loopsPerformance tuning tips to optimize latency and generation qualityPractical tools: Prometheus, Grafana, LangSmith, and moreDrift detection, caching strategies, and security implementationFully documented code examples for step-by-step learningInsights into the future of multimodal and agentic RAG systemsThis book is perfect for AI developers, machine learning engineers, and data scientists building LLM-based applications. If you've already worked with RAG or LLM pipelines and want to push them to production-ready quality, this guide is your go-to resource. It's also ideal for backend engineers integrating AI models into microservices and product managers overseeing intelligent features. If you're ready to move from experimental to enterprise-grade AI systems, Debugging and Optimizing RAG Pipelines gives you the tools and confidence to do just that. Get your copy now and take control of your RAG pipeline's performance, reliability, and accuracy-because building smarter AI starts with better engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Condición: New. Print on Demand.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Debugging and Optimizing RAG Pipelines: A Practical Guide for AI DevelopersThis hands-on guide explores the challenges and solutions of building high-performance Retrieval-Augmented Generation (RAG) pipelines. As AI-powered applications become more complex, understanding how to monitor, debug, and fine-tune RAG systems is essential. This book provides a clear and practical roadmap for developers working with large language models, search engines, and generation components to ensure reliability, accuracy, and efficiency in production-grade AI systems.From real-time monitoring to error tracing and optimization techniques, this book walks you through every stage of a RAG pipeline. Whether you're troubleshooting hallucinations, improving retrieval quality, or scaling a system for enterprise use, you'll find actionable guidance and ready-to-use code examples that save time and reduce friction. Debugging and Optimizing RAG Pipelines goes beyond just theory. It addresses real-world challenges that AI developers face when building and deploying RAG systems. You'll learn to identify issues early, implement observability tools, reduce latency, eliminate hallucinations, and continuously improve system performance. Each chapter includes practical tips, hands-on examples, and expert insights designed to help you create RAG pipelines that are robust, scalable, and easy to maintain.Key Features of This BookReal-world debugging workflows for complex RAG systemsBest practices for prompt design, logging, and feedback loopsPerformance tuning tips to optimize latency and generation qualityPractical tools: Prometheus, Grafana, LangSmith, and moreDrift detection, caching strategies, and security implementationFully documented code examples for step-by-step learningInsights into the future of multimodal and agentic RAG systemsThis book is perfect for AI developers, machine learning engineers, and data scientists building LLM-based applications. If you've already worked with RAG or LLM pipelines and want to push them to production-ready quality, this guide is your go-to resource. It's also ideal for backend engineers integrating AI models into microservices and product managers overseeing intelligent features. If you're ready to move from experimental to enterprise-grade AI systems, Debugging and Optimizing RAG Pipelines gives you the tools and confidence to do just that. Get your copy now and take control of your RAG pipeline's performance, reliability, and accuracy-because building smarter AI starts with better engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …