In software development, we never rely on conversational guesswork when a deterministic utility exists. We build CI pipelines that run automated test suites, validate strict schemas, and filter logs before browsing them with zero tokens spent.
Yet in GenAI engineering, we can routinely over-delegate to the model without thinking: asking an LLM to self-reflect, calculate values for us, or check for specific strings in the code and make a todo list of items to clean up.
Caution
Self-Reflection can Compounds Hallucinations: Asking an LLM to verify its own logic invites confirmation bias into the loop. Once a model hallucinates, subsequent conversational checks often rationalize the original error instead of catching it.
Only pay the model for reasoning, don't pay it to act like an nondeterministic (unpredictable) version of a script we could've ran instead.