// Do some work
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The case went to trial, and testimony began last week in US District Court for the Southern District of New York. But the US and Live Nation informed the court of a proposed settlement on March 8, taking state attorneys general by surprise. The judge presiding over the case reportedly said in court today that the way the settlement was announced "is absolutely unacceptable."
The total encoding cost includes all the work that goes in to writing a prompt, and all of the compute required to run the prompt. If the task is simple to express in a prompt, the total encoding cost is low. If the task is both simple to express in a prompt, and tedious or difficult to produce directly, the relative encoding cost is low. As models get more capable, more complex prompts can be easily expressed: more semantically dense prompts can be used, referencing more information from the training data. An agent capable of refining or retrying a task after an initial prompt might succeed at a complex task after a single simple prompt. However, both of these also increase the compute cost of the prompt, sometimes substantially, driving up the total encoding cost. More “capable” models may have a higher probability of producing correct output, reducing costs reprompting with more information (“prompt engineering”), and possibly reducing verification costs.
https://github.com/kyx0r/nextvi/tree/test