Cognitive control relies on neural representations that are inherently high-dimensional and distributed across multiple subregions in the prefrontal cortex (PFC). Traditional approaches tackle prefrontal representation by reducing it into a unidimensional measure (univariate amplitude) or using it to distinguish a limited number of alternatives (pattern classification). In contrast, representational similarity analysis (RSA) enables flexibly formulating various hypotheses about informational contents underlying the neural codes, explicitly comparing hypotheses, and examining the representational alignment between brain regions. Here, we used a multifaceted paradigm wherein the difficulty of cognitive control was manipulated separately for five cognitive tasks. We used RSA to unveil representational contents, measure the representational alignment between regions, and quantify representational generality versus specificity. We found a graded transition in the lateral PFC: The dorsocaudal PFC was tuned to task difficulty (indexed by reaction times), preferentially connected with the parietal cortex, and representationally generalizable across domains. The ventrorostral PFC was tuned to the abstract structure of tasks, preferentially connected with the temporal cortex, and representationally specific. The middle PFC (interposed between the dorsocaudal and ventrorostral PFC) was tuned to individual task sets and ranked in the middle in terms of connectivity and generalizability. Furthermore, whether a region was dimensionally rich or sparse covaried with its functional profile: Low dimensionality (only gist) in the dorsocaudal PFC dovetailed with better generality, whereas high dimensionality (gist plus details) in the ventrorostral PFC corresponded with better ability to encode subtleties. Our findings, collectively, demonstrate how cognitive control is decomposed into distinct facets that transition steadily along prefrontal subregions.