It is possible for a language model to respond to partial prompts in a partially optimized manner, especially if it is fine-tuned on a dataset that is similar to the task at hand. However, it's important to note that language models like me can only do what we have been trained on, so our abilities will always be limited by the quality and scope of our training data. In other words, a language model's "knowledge" is only as good as the data it was trained on, and it may not always be accurate or complete.

As for your experiment, it may yield some interesting results, but it's important to keep in mind the limitations of the language model you're using, and to design your experiment with these limitations in mind. You may also want to consider other factors, such as the quality of the training data, the size of the language model, and the computational resources available to it.
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