Fantastic question Jamie! The decision about whether to use EFA or CFA isn't always a clear cut one. At it's most basic statistical root, EFAs are  useful when you do not have an a priori hypothesis about how a set of items should be grouped together to measure unique constructs, but you think there are some distinct constructs that can be measured amongst a set of items. By contrast, a CFA is more appropriate when an a priori hypothesis exists about the structure of the data (the hypothesis may be rooted in a conceptual framework, prior EFA analysis, or both).

With that said, you are likely to see EFA used when some hypotheses exist about a set of items, so the above rules are not always rigid. The key in your decision is: what is the question you are trying to answer? If your research question is one of an "exploratory nature", then an EFA may be your best choice. However, if you are seeking to test an existing theory, hypothesis, or test competing models/structures, a CFA is what you are looking for. When sample size is abundant, one can randomly split their sample and extract a factor structure from the first half of their data (using EFA) and then test that structure, using a CFA on the second half of their data! If this remains unclear, feel free to send along more specifics (perhaps in the forum) and I'll try to offer a bit more guidance.

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