Context Engineering for LLMs is the operational handbook for anyone who wants LLMs to behave predictably, efficiently, and responsibly in production. The book reframes prompt engineering as a systems discipline context pipelines that include chunking, compression, vectorization, retrieval orchestration, and memory layers. You’ll learn token-economics for large context windows, practical prompt architectures, system and user message patterns, and templates for common tasks.
The middle sections cover embedding strategies, vector-store patterns, and RAG designs that ensure relevance, freshness, and cost control. Memory system chapters describe how to design short-term and long-term memory, when to use episodic memory, and how to index and expire context. Security and reliability are core themes: learn prompt injection defenses, context validation, and audit trails. The book closes with evaluation, A/B testing, CI pipelines for prompt changes, and operational patterns for continuous improvement.
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Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. Context Engineering for LLMs is the operational handbook for anyone who wants LLMs to behave predictably, efficiently, and responsibly in production. The book reframes prompt engineering as a systems discipline context pipelines that include chunking, compression, vectorization, retrieval orchestration, and memory layers. You'll learn token-economics for large context windows, practical prompt architectures, system and user message patterns, and templates for common tasks.The middle sections cover embedding strategies, vector-store patterns, and RAG designs that ensure relevance, freshness, and cost control. Memory system chapters describe how to design short-term and long-term memory, when to use episodic memory, and how to index and expire context. Security and reliability are core themes: learn prompt injection defenses, context validation, and audit trails. The book closes with evaluation, A/B testing, CI pipelines for prompt changes, and operational patterns for continuous improvement.What's inside: Practical prompt templates and system-message strategies for structured outputs.Token cost modeling and context-window optimization techniques.Chunking, semantic compression, and fragment re-assembly recipes.Embedding strategies, batching, and hybrid index maintenance.Memory architectures: ephemeral, episodic, and persistent memory designs.RAG workflows and retrieval orchestration best practices.Prompt-injection defenses, content filtering, and context validation checks.Evaluation frameworks, metrics, and test suites for context quality.CI/CD for prompts and context pipelines, plus A/B testing patterns.Operational playbooks for latency, scaling, and cost tradeoffs.Who this book is for: Prompt engineers, ML engineers, SREs, and product teams shipping LLM features.Teams that require predictable, auditable, and cost-effective LLM behavior. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798265876911
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 51310924-n
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: CitiRetail, Stevenage, United Kingdom
Paperback. Condition: new. Paperback. Context Engineering for LLMs is the operational handbook for anyone who wants LLMs to behave predictably, efficiently, and responsibly in production. The book reframes prompt engineering as a systems discipline context pipelines that include chunking, compression, vectorization, retrieval orchestration, and memory layers. You'll learn token-economics for large context windows, practical prompt architectures, system and user message patterns, and templates for common tasks.The middle sections cover embedding strategies, vector-store patterns, and RAG designs that ensure relevance, freshness, and cost control. Memory system chapters describe how to design short-term and long-term memory, when to use episodic memory, and how to index and expire context. Security and reliability are core themes: learn prompt injection defenses, context validation, and audit trails. The book closes with evaluation, A/B testing, CI pipelines for prompt changes, and operational patterns for continuous improvement.What's inside: Practical prompt templates and system-message strategies for structured outputs.Token cost modeling and context-window optimization techniques.Chunking, semantic compression, and fragment re-assembly recipes.Embedding strategies, batching, and hybrid index maintenance.Memory architectures: ephemeral, episodic, and persistent memory designs.RAG workflows and retrieval orchestration best practices.Prompt-injection defenses, content filtering, and context validation checks.Evaluation frameworks, metrics, and test suites for context quality.CI/CD for prompts and context pipelines, plus A/B testing patterns.Operational playbooks for latency, scaling, and cost tradeoffs.Who this book is for: Prompt engineers, ML engineers, SREs, and product teams shipping LLM features.Teams that require predictable, auditable, and cost-effective LLM behavior. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798265876911
Quantity: 1 available