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.
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