Context Engineering for LLMs Applications
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Home > Computing and Information Technology > Computer science > Artificial intelligence > Natural language and machine translation > Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications(3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)
Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications(3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)

Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications(3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)


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About the Book

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.


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Product Details
  • ISBN-13: 9798265876911
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 212
  • Returnable: N
  • Spine Width: 11 mm
  • Weight: 426 gr
  • ISBN-10: 8265876912
  • Publisher Date: 17 Sep 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: 3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs
  • Sub Title: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications
  • Width: 178 mm


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Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications(3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)
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Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications(3 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)

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