Nolan Stark (77 results)

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

Taschenbuch. Condition: Neu. Neuware - AI can generate code quickly. Becoming a better developer requires knowing what to build, how to verify it, and why the resulting code works.Becoming a Better Developer with Claude presents a practical way to use AI throughout the modern software development lifecycle without becoming dependent on generated answers.You will learn how to prepare a clean project, give an AI coding assistant the context it needs, write clearer instructions, plan complex changes, evaluate design choices, and break large tasks into small, reviewable steps.The book then moves into everyday engineering work: writing code, debugging, reproducing failures, tracing root causes, creating tests, reviewing changes, writing documentation, deploying software, and maintaining existing codebases.A major theme is verification. A plausible AI-generated answer is not the same thing as a verified engineering solution. You will learn how to turn vague symptoms into reproducible failures, connect fixes to evidence, add tests that prove behavior, and review generated changes before they become part of a real system.The goal is not to make you a faster copier of AI-generated code. It is to help you become a sharper developer with an AI coding partner that handles mechanical work while you retain engineering judgment.For programmers, software engineers, technical leads, and developers adopting AI-assisted coding, this book provides a practical framework for working faster without weakening the habits that make software reliable.…

Taschenbuch. Condition: Neu. Neuware - Building software with a language model is fundamentally different from chatting with one through a consumer interface.Building AI Applications with the Claude API provides a practical introduction to integrating language intelligence into software, services, tools, and digital products.You will learn what an AI API actually does, how messages and roles work, how tokens and context windows affect application behavior, how to create and protect API credentials, and how to structure a project around an SDK or direct HTTP requests.The book then moves into the building blocks of useful AI applications. You will learn how to design system prompts, use examples, create tools, handle function calling, stream responses, request structured output, process documents and images, and combine multiple steps into a complete application feature.The final section addresses production engineering: AI-specific failure modes, validation, guardrails, model selection, token costs, latency, caching, scaling, security, privacy, and responsible deployment.For backend developers, full-stack developers, technical founders, software engineers, and builders integrating language models into applications, this book provides a practical bridge between API experimentation and production software.It focuses on the engineering decisions that matter after the first successful request-how to make the integration reliable, maintainable, cost-aware, secure, and useful to real users. …

Taschenbuch. Condition: Neu. Neuware - Taking a generative AI application from a promising prototype to a reliable, enterprise-wide system takes a lot more than good prompting - it takes real architectural discipline. This book brings together advanced retrieval strategies, cost optimization, and behavioral control into one complete framework for context engineering done right.At its core, the book tackles the central challenge every serious AI deployment faces: feeding models the external, proprietary data they need without overwhelming their attention or blowing through your API budget. You'll get a full walkthrough of Anthropic's prompt caching system - structuring conversations so identical system prompts and tool definitions sit at the front, unlocking up to 90% cost savings and 85% faster response times on every call - along with strategies for managing cache state across long, multi-turn conversations.Beyond efficiency, there's a strong focus on accuracy. The book lays out a rigorous approach to Retrieval-Augmented Generation, covering contextual chunking, contextual embeddings, and semantic reranking to cut retrieval failures by nearly half and meaningfully reduce hallucinations caused by missing context. The throughline is treating context engineering as a genuine architectural discipline, not an afterthought - so your applications can reason accurately over massive datasets without falling apart at scale.This is essential reading for AI product managers, senior software engineers, and technical founders serious about scaling generative applications the right way.…