Llms practice (45 résultats)

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Taschenbuch. Etat : Neu. Neuware - LLMs and Small Language Models focuses on how different model sizes influence the design and performance of AI applications. It presents a balanced view of when to use large language models and when smaller models offer a more practical solution. Rather than assuming that larger models are always better, the book examines how efficiency, responsiveness, and deployment constraints shape real-world decisions.The material explores how applications can be designed with model selection in mind, showing how different tasks benefit from different levels of model capability. It highlights how smaller models can provide speed and cost advantages, while larger models offer broader reasoning and flexibility. By understanding these tradeoffs, readers can make informed decisions about how to structure their systems for both performance and practicality.Attention is also given to how models are integrated into applications, including how they interact with retrieval systems, workflows, and user-facing features. The book presents model choice as part of a larger design process, where each component contributes to the overall behavior of the system. This perspective helps readers build applications that are not only capable, but also efficient and well suited to their intended environment.…

Taschenbuch. Etat : Neu. Neuware - RAG in Practice: Engineering Reliable Retrieval-Augmented Generation Systems for LLMs and Generative AI Unlock the full potential of Retrieval-Augmented Generation (RAG) with this authoritative, hands-on guide for engineers, AI professionals, and data scientists. RAG in Practice bridges the gap between large language models (LLMs) and enterprise knowledge systems, teaching you how to design, implement, and optimize robust, production-ready RAG pipelines.Inside this book, you'll master: - RAG Fundamentals: Understand why standalone LLMs are limited, how RAG enhances reasoning, and the evolution from IR + NLP to modern retrieval-augmented systems.- RAG System Architecture: Explore minimal and high-level pipelines, online/offline components, and data/control flow engineering.- Embeddings & Vector Databases: Learn dense vs sparse embeddings, embedding drift, ANN algorithms, hybrid search, and large-scale vector indexing.- Retrieval Quality Engineering: Implement similarity metrics, top-K selection, reranking with cross-encoders, and handle retrieval failures.- Document Ingestion Pipelines: Design batch, streaming, and hybrid ingestion; handle PDFs, tables, HTML; and implement chunking strategies with overlap and context awareness.- Data Quality & Versioning: Apply cleaning, normalization, deduplication, versioning, rollbacks, and audit strategies for enterprise-grade reliability.- Query Processing & Intelligence: Master query classification, rewriting, multi-query retrieval, and self-querying RAG systems.- Advanced Retrieval Techniques: Build hybrid search, temporal/context-aware retrieval, and multi-hop systems for real-world applications.This book is packed with Python code examples, architecture diagrams, and practical guidance, so you can implement systems confidently while avoiding common production pitfalls.Case Studies Included- Large-Scale Vector Search - industrial vector database deployment and performance optimization.- Enterprise Document Ingestion - handling multi-format documents at scale.- Search-Driven RAG at Scale - hybrid search and multi-hop retrieval in production.- RAG Retrieval Failures - diagnosis and mitigation of low recall/high hallucination scenarios.- Knowledge Base Versioning - version control and rollback in live systems.Whether you're building enterprise search, AI assistants, or knowledge-grounded LLM applications, RAG in Practice provides the step-by-step blueprint to engineer high-performance, reliable, and scalable knowledge-augmented AI systems. …

Taschenbuch. Etat : Neu. Neuware - Most developers learn what LLMs can do. This book teaches you how>Large Language Models in Practice is a hands-on engineering guidefor developers who want to go beyond ChatGPT prompts and build>You'll start with the fundamentals transformers, tokenization, embeddings, attention and progressively move into the engineeringpatterns that power real-world AI products. Every concept is paired>What's inside: - How LLMs actually work under the hood transformers, self-attention, positional encoding, and next-token prediction- Working with LLM APIs authentication, context windows, streaming, cost management, and rate limits - Prompt engineering that works zero-shot, few-shot, chain-of-thought, role-based prompting, and reusable templates- Building real AI apps chatbots, summarizers, content generators, and information extraction systems- Retrieval-Augmented Generation (RAG) vector databases, embeddings, document chunking, and full RAG pipelines- Fine-tuning open-source models for your specific use case- AI agents how to design, build, and orchestrate them- Production deployment scaling, monitoring, evaluation, >10 hands-on projects including a PDF Q&A system, AI customersupport chatbot, personal research assistant, and a deployable>This is not a theory textbook. This is the book you hand to>Perfect for: software engineers, backend developers, technicalfounders, CS students, and self-taught developers who want tobuild serious AI systems no ML PhD required. …

Paperback. Etat : new. Paperback. LLMs and Small Language Models focuses on how different model sizes influence the design and performance of AI applications. It presents a balanced view of when to use large language models and when smaller models offer a more practical solution. Rather than assuming that larger models are always better, the book examines how efficiency, responsiveness, and deployment constraints shape real-world decisions.The material explores how applications can be designed with model selection in mind, showing how different tasks benefit from different levels of model capability. It highlights how smaller models can provide speed and cost advantages, while larger models offer broader reasoning and flexibility. By understanding these tradeoffs, readers can make informed decisions about how to structure their systems for both performance and practicality.Attention is also given to how models are integrated into applications, including how they interact with retrieval systems, workflows, and user-facing features. The book presents model choice as part of a larger design process, where each component contributes to the overall behavior of the system. This perspective helps readers build applications that are not only capable, but also efficient and well suited to their intended environment. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Etat : new. Paperback. Are you ready to build cutting-edge AI applications with confidence? LLMs in Practice: A Hands-On Guide to Building and Integrating AI Applications Using LangChain and Hugging Face by Seth Harvey is your practical roadmap to creating powerful, real-world AI solutions. This book equips developers, data scientists, and tech enthusiasts with the skills to harness large language models (LLMs) effectively, using two of the most popular open-source frameworks: LangChain and Hugging Face.The book focuses on actionable techniques for building AI applications, from crafting effective prompts to deploying scalable services. It guides you through integrating LLMs with external data, tools, and memory to create intelligent systems like chatbots, knowledge bots, and automation agents. With clear explanations and hands-on projects, you'll learn to combine LLMs with LangChain's flexible workflows and Hugging Face's robust models, making complex AI development accessible and efficient.What sets this book apart?Prompt Engineering: Master crafting prompts to guide LLMs for precise, context-aware responses.Retrieval-Augmented Generation: Learn to ground LLMs with external data for accurate, reliable outputs.Agents and Workflows: Build dynamic agents and multi-agent systems to tackle complex tasks.Practical Use Cases: Apply LLMs to real-world projects like coding assistants and e-commerce recommenders.Fine-Tuning and Deployment: Customize models and deploy applications with FastAPI, Streamlit, or Hugging Face platforms.Best Practices: Ensure security, ethics, and performance with actionable strategies.Readers will gain practical skills to create AI applications that solve real problems, backed by code examples and expert insights. Whether you're a beginner or an experienced developer, this book provides the tools to build, customize, and deploy AI solutions with confidence.Don't miss out on mastering AI development! Grab your copy of LLMs in Practice today and start building intelligent applications that stand out. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. Build real-world AI assistants, not just toy demos.LLMs in Practice shows you, step by step, how to build retrieval-augmented generation (RAG) systems with embeddings and vector databases, then turn them into production-ready assistants that search, reason, and take action.Instead of hand-wavy theory, this book walks through a complete stack: ingesting documents, chunking and embedding them, storing vectors, wiring up retrieval, designing grounded prompts, evaluating quality, logging behaviour, securing data, adding tools, and finally deploying everything as a service. Along the way, you see the same patterns implemented in both Python and TypeScript, so you can work in whichever ecosystem you prefer.You'll learn how to take a messy folder of PDFs, wikis, and docs and turn it into: A searchable knowledge base backed by embeddings and a vector databaseA grounded RAG pipeline that cites its sources instead of hallucinatingA tools-enabled assistant that not only answers questions, but can create tickets, trigger workflows, or call APIsAn observable system with traces, logs, and a small evaluation set, so you can improve it over timeA deployable service (FastAPI or Express) that real users can talk toThe focus throughout is on small, composable building blocks you can actually ship: tight retrieval functions, clear prompt templates, thin adapters around model providers, and simple web endpoints that wrap it all together. No heavy frameworks required.By the end of the book, you'll have a practical roadmap to go from "I can call an LLM API" to "I have a narrow, grounded assistant in production that my team actually uses"-and a set of patterns you can reuse for the next assistant you build. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. LLMs in Practice Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the transformative impact of artificial intelligence (AI) on the field of translation. As technology advances, AI-driven tools and methodologies are reshaping traditional translation workflows, redefining the roles of translators, and enhancing both efficiency and accuracy in translation tasks. Through comprehensive analyses and case studies, this book offers valuable insights into how AI is driving innovation in both translation practice and education, fostering a future where human creativity and technological advancements work in tandem to meet the demands of an increasingly globalized world.…