context-engineering

AI agent context management improves output quality by designing a deliberate context stack with rules, specs, and source files, applying structured loading strategies to keep behavior aligned with project conventions.

AI session hallucinations during task switching often occur from inconsistent rules or excessive context. Per-feature spec-level context loading keeps prompts relevant and minimizes hallucinations by loading only necessary source files.

Hierarchical context loading is a strategy that packages persistent rules files, per-feature specs, and relevant source files into a structured stack, ensuring AI behavior remains aligned with project conventions across sessions.

Start a fresh AI session by loading your project rules file, then apply a context-refresh workflow to selectively load relevant spec and source files, maintaining focus during task switches and project transitions.

You can persist project rules across multiple AI coding sessions by using persistent rules files, such as CLAUDE.md equivalents, which maintain consistent conventions and prevent brittle prompts throughout session transitions.

Handle error outputs and conversation history by integrating error-output guidance and applying structured history management workflows, which maintain session focus and prevent context overflow during complex project development.