Articoli correlati a Context Engineering for LLMs Applications: Designing...

Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications: 3 - Brossura

Libro 3 di 4: Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs

Zhang, Carter

 
9798265876911: Context Engineering for LLMs Applications: Designing prompts, memory, retrieval and context pipelines for reliable, cost-effective LLM applications: 3

Sinossi

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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