context-engineering

To stop agent hallucinations and enforce project conventions, curate the agent context by maintaining persistent rules files, loading only relevant spec sections and source files, and iteratively feeding concise error outputs back into the agent.

Context engineering for AI-assisted development is the process of optimizing agent context setup through selective loading of spec sections, conversation compaction, and explicit trust-level handling to ensure reliable, non-hallucinated outputs across coding sessions.

Set up a persistent rules file by capturing your tech stack, commands, conventions, and boundaries, then instruct the agent to follow those conventions before making changes to maintain consistent code assistance across sessions.

Coding agents lose focus and produce degraded output due to context flooding from loading too many source files and stale instructions, which you can fix by summarizing, compacting, and rotating the session context.

Compact or rotate conversation context when switching between features, starting a new coding session, or diagnosing degraded agent output to prevent stale or contradictory instructions from accumulating in the context window.

Yes, you can prevent context flooding when diagnosing agent errors by selectively loading only the relevant spec sections and source files, and iteratively including concise error output and test failures to drive precise fixes.