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Large Language Models in Practice: How LLMs Work, How to Use Them, and How to Build Production-Ready AI Systems - Softcover
Inhaltsangabe
Most developers learn what LLMs can do. This book teaches you how
to actually build with them.
Large Language Models in Practice is a hands-on engineering guide
for developers who want to go beyond ChatGPT prompts and build
real, production-ready AI systems from scratch, with working code.
You'll start with the fundamentals transformers, tokenization,
embeddings, attention and progressively move into the engineering
patterns that power real-world AI products. Every concept is paired
with Python code you can run, modify, and ship.
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,
and enterprise-grade architecture
10 hands-on projects including a PDF Q&A system, AI customer
support chatbot, personal research assistant, and a deployable
enterprise AI assistant.
This is not a theory textbook. This is the book you hand to
a developer and say: build something real with it.
Perfect for: software engineers, backend developers, technical
founders, CS students, and self-taught developers who want to
build serious AI systems no ML PhD required.
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Paperback. Zustand: new. Paperback. Most developers learn what LLMs can do. This book teaches you howto actually build with them. Large Language Models in Practice is a hands-on engineering guidefor developers who want to go beyond ChatGPT prompts and buildreal, production-ready AI systems from scratch, with working code. 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 pairedwith Python code you can run, modify, and ship. 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, and enterprise-grade architecture 10 hands-on projects including a PDF Q&A system, AI customersupport chatbot, personal research assistant, and a deployableenterprise AI assistant. This is not a theory textbook. This is the book you hand toa developer and say: build something real with it. Perfect for: software engineers, backend developers, technicalfounders, CS students, and self-taught developers who want tobuild serious AI systems no ML PhD required. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798182463218
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