How does context budgeting work?

It involves setting explicit token limits, monitoring usage during development, and implementing triggers for context compaction when utilization reaches 70-80%.

What is the primary goal of context engineering?

The primary goal is to curate the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes, treating context as a finite resource.

Why does model performance degrade in large context windows?

As context grows, the attention mechanism's ability to capture pairwise relationships between all tokens is stretched thin, leading to reduced precision in retrieval and reasoning.

What is the Progressive Disclosure Principle?

It is a strategy to manage context efficiently by loading information only as it is needed, such as loading full skill content only after a skill is activated.

Where should critical information be placed in a prompt?

Critical information should be placed at attention-favored positions, which are typically at the beginning and the end of the context window.

Context Engineering Fundamentals

Name: Context Engineering Fundamentals
Author: goodnight000

bygoodnight000

데이터 과학 및 ML

Master the core principles and mechanics of context management to build more efficient and accurate AI agent architectures.

소개

This skill provides a comprehensive framework for understanding how context functions within agentic systems. It explores the 'attention budget' constraint of language models, detailng the anatomy of context including system prompts, tool definitions, and message history. By mastering techniques like progressive disclosure and context budgeting, developers can optimize token usage, reduce costs, and prevent model degradation during long-horizon tasks. This is an essential resource for anyone designing, debugging, or scaling complex AI-driven workflows.

주요 기능

Strategies for optimizing attention distribution and model precision
Practical guidance on file-system-based context access and hybrid loading
In-depth analysis of context components: system prompts, tools, and history
Principles of progressive disclosure for efficient token management
0 GitHub stars
Structural prompt design patterns using XML and Markdown

사용 사례

Architecting new agent systems with strict token constraints
Refining existing prompts to improve retrieval accuracy and model focus
Debugging agent failures related to context saturation or degradation

소개

주요 기능

Strategies for optimizing attention distribution and model precision
Practical guidance on file-system-based context access and hybrid loading
In-depth analysis of context components: system prompts, tools, and history
Principles of progressive disclosure for efficient token management
0 GitHub stars
Structural prompt design patterns using XML and Markdown

사용 사례

Architecting new agent systems with strict token constraints
Refining existing prompts to improve retrieval accuracy and model focus
Debugging agent failures related to context saturation or degradation