Prompt changes can unintentionally alter task framing, leading to valid but incorrect outputs.

LLMs are highly sensitive to instruction wording, ordering, and context length. A prompt that works during testing may fail once additional system messages or user inputs are added.

To prevent this, version-control prompts and test them with adversarial and edge-case inputs. Keep instructions explicit and avoid mixing multiple objectives in a single prompt.

If outputs suddenly degrade, diff the prompt text before blaming the model.

Common mistakes:

• Relying on implicit instructions

• Appending user input without separators

• Assuming prompts are stable across model versions

Treat prompts as code, not static text.