context-engineering

To stop AI coding agents from hallucinating APIs, you need context engineering to organize project rules and source files into a structured loading hierarchy. This ensures the agent receives only relevant information, enforcing repository patterns and preventing convention mismatches.

Context engineering for AI coding workflows is the practice of organizing project context, rules, and specifications into a structured loading hierarchy. It manages task-specific information and ambiguity handling workflows to deliver reliable coding assistance.

Structure project rules by designing a context strategy that organizes specifications, source files, and task information into an effective loading hierarchy. This guided diagnosis targets convention mismatches and prompt overload to directly fix agent output quality.

AI agents experience context drift during codebase navigation due to poor context organization and excessive irrelevant information loading. Without a structured context strategy defining loading hierarchies, the agent loses focus on task-specific project conventions.

The best way to set up an AI coding agent session is applying context engineering to define loading hierarchies and project conventions. This creates focused context files that optimize the agent's understanding of your specific repository patterns.