So interesting, thank you. A human (who presumably wanted another human to successfully execute this bike ride) would perceive this task very differently, prioritizing descriptions of places that actually exist and meaningful content over structure of the response. In this case the only relevant information in the output was essentially provided in the question, i.e., that there would be a bike and the C&O Canal was somehow involved. As I think I understand you, using a relatively small and undifferentiated model decreased the probability that the guesses would be at all relevant. A smaller but selected model (say, incorporating a hundred thousand actual conversations about biking on the C&O) might more likely produce relevant output. The analogous switchboard in the human brain would be the frontal cortex, filtering and interpreting the question to understand the correct priorities in context.

My two cents :
I'm an old psychiatrist and psychoanalyst, I'm sorry to see how many of my younger fellows works with all those scales : MADRS, PANSS, PSYRATS, BAVQ-R, Y-BOCS, CGI and so on, these tools are indeed extremely AI-adaptable.
Their reliability is unfortunately poor, compared to a non or semi structured interview.
As for bike touring, I plan vaguely and mostly interact with local humans for the real "POI".

I'm patiently waiting for the next AI winter.