Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications ... Context, and Knowledge Graphs, Band 3) - Softcover

Inhaltsangabe

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.

• 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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