A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?
Temperature? What is this, a cooking exam? I'm pretty sure that's not gonna help with sentiment analysis on Amazon Bedrock. Maybe they should try the 'Preheat to 350°F' option instead.
Model size, all the way! The bigger the model, the more it can fit into a single prompt. It's like trying to cram an entire library into a backpack - you need a bigger backpack!
Batch size? Really? I guess if they're running a whole bunch of prompts at once, it could be a factor. But come on, let's focus on the important stuff here - the model size and the context window.
Context window, for sure! The company needs to know how much context the LLM can take in at once. It's like trying to read a book without the previous chapters - you just can't get the full story.
Context window, for sure! The company needs to know how much context the LLM can take in at once. It's like trying to read a book without the previous chapters - you just can't get the full story.
Hmm, I'd say the model size is the key consideration here. The larger the model, the more information it can handle in a single prompt. But don't forget, you've got to have the compute power to back it up!
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