
Amazon Bedrock & BabyAGI (with Jon Turow)
Newcomer Podcast
How to Create a Thinking Pattern for Large Models
These large models are trained on a huge corpus of facts, but they're actually best thought of as reasoning machines, not fact machines. And so what we really want to do is feed those facts into the model as part of the chain. This is the data stack. To retrieve that information. So let's take an example. Let's say that Eric newcomer wants to take a trip from New York to LA. Now the LLM could reason that the steps to do that are find out what the data stack is. And then find out what flights match those preferences. And then suggest a bunch of options that match the preferences.
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