ai-context-patterns - Concepts

Explore concepts tagged with "ai-context-patterns"

Total concepts: 7

Concepts

Context-as-Code - Practice of treating AI context definitions as version-controlled, reviewable, and testable code artifacts rather than ephemeral prompt text.
Context Anchoring - Practice of externalizing decision context into persistent, version-controlled documents that survive across AI sessions to guide consistent behavior.
Context Layering - Architectural pattern of organizing AI context into hierarchical layers with defined scope, precedence, and inheritance from global to task-specific.
Context Lifecycle - The full operational cycle of AI context from creation through maintenance, review, evolution, and eventual retirement.
Context File Hierarchy - Structured organization of context files like CLAUDE.md and AGENTS.md at different directory levels that compose into layered AI instructions through top-down merging.
Context Hygiene - Practices for actively managing, pruning, and maintaining the quality of AI context throughout its lifecycle to prevent degradation.
Context Budget - Deliberate allocation of a model's finite context window across different types of context, framing context engineering as an optimization problem with hard token constraints.