The analysis tool was asked to research an API for PowerPoint generation using Python, and for how to apply styles.
When asked a followup question about whether a piece of known-working code was valid, it confidently reported that the code was not valid, as the code did not conform to the API calls discovered in its previous research turn.
Instead of reporting that the code was invalid, it should have attempted to fetch more information on the topic, to see if there was more information outside of the scope of its original research.
Dishonesty: The AI confidently reported a fact as true that could be proven otherwise
Overeagerness: The AI skipped any followup research steps
Context-Poisoning: The AI assumed that the information present in its knowledge base was all the knowledge on the subject that was available.
The analysis tool was asked to research an API for PowerPoint generation using Python, and for how to apply styles.
When asked a followup question about whether a piece of known-working code was valid, it confidently reported that the code was not valid, as the code did not conform to the API calls discovered in its previous research turn.
Instead of reporting that the code was invalid, it should have attempted to fetch more information on the topic, to see if there was more information outside of the scope of its original research.
Dishonesty: The AI confidently reported a fact as true that could be proven otherwise
Overeagerness: The AI skipped any followup research steps
Context-Poisoning: The AI assumed that the information present in its knowledge base was all the knowledge on the subject that was available.