Context engineering is the practice of curating the information that large language models receive at inference time so that the model can perform a task reliably and cost-effectively. It treats the context window as a finite attention budget and looks for the smallest set of high-signal tokens that maximize the likelihood of the desired outcome. Context engineering subsumes and extends prompt engineering, system prompts, tool design, retrieval, agent loops, structured note taking, compaction, and multi-agent decomposition.
URL: Visit APIs.json URL
- Agents, AI, Anthropic, Compaction, Context Window, LLM, Memory, Prompt Engineering, RAG, Tools
- Created: 2025-01-01
- Modified: 2026-04-28
Anthropic's engineering guide framing context as a finite attention budget and walking through system prompts, tool design, just-in-time retrieval, compaction, structured notes, and multi-agent architectures.
Human URL: Anthropic engineering blog
Pattern that augments LLM prompts with passages retrieved at inference time from a vector store, search index, or knowledge base.
Human URL: Original RAG paper
The discipline of crafting model instructions and examples to guide model behavior, including chain-of-thought, few-shot, and structured output formats.
Human URL: https://www.promptingguide.ai/
Iterative reasoning patterns where an LLM plans, calls tools, observes results, and refines its plan.
Human URL: Tool use overview
Compaction, structured note taking, and multi-agent decomposition for tasks that exceed the context window.
Human URL: Contextual retrieval
FN: Kin Lane
Email: kin@apievangelist.com