Structured prompts beat rule lists for stopping AI agents from inventing fake code

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AI coding agents like Claude Code and Cursor frequently generate plausible-looking code that references methods or functions that do not exist in the actual codebase. Developers typically respond by adding more restrictive rules to their prompts, but this approach fails because the model must hold all instructions in working memory while focusing on the task, causing rules to be silently dropped under cognitive load. The root cause is structural: the prompt never requires the agent to verify that a method exists before writing code that calls it. A more effective approach is to replace prohibitions with an ordered procedure, first requiring the agent to list and confirm every method it plans to use, and only then allowing it to write the code. This structural change eliminates the entire class of hallucinated-method errors rather than patching individual instances.

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