Comment by Applejinx
1 day ago
Huh. Alternate explanation: there's a layer of indirection, drawing upon the unthinkable size of the source data, so rather than 'issue forth tokens as if there is a person answering a question', you've got 'issue forth tokens as if there is a person being challenged to talk about their process', something that's also in the training data but in different contexts.
I'm not sure statements of 'aha, I see it now!' are meaningful in this context. Surely this is just the em-dash of 'issue tokens to have the user react like you're thinking'?
I wonder if something else is going on, and perhaps Claude is using the LLM to identify the likely culprits within the codebase, sending the code around them to execute with an actual Python interpreter on their servers, feeding both the code and the result as the context window to another LLM query with a system prompt something like "What is this code doing, when it runs on this input and this output?", feeding the result of that back to the user, and then repeating as long as the overall bug remains unsolved. I've found that feedback is a very effective technique with LLMs, asking them to extract some data, testing that data through out-of-band mechanisms, then feeding the test results and the original context back into the LLM to explain its reasoning and why it got the result. The attention mechanisms in the transformer model function very well when they're prompted with specifics and asked to explain their reasoning.
Only an Anthropic engineer would know for sure. I'm pretty sure that it was making multiple queries on my behalf during the chat transcript - each "Read ... mp3organizer_v2.py" is a separate network round-trip.