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Crowdsourcing has become a popular choice for tackling problems that neither computers nor humans alone can solve with adequate speed, cost, and quality. However, instructing crowds to execute tasks in the manner expected by the requesters is challenging. It depends on not only requesters’ task design abilities but also workers’ understanding of the tasks. Task design bridges the communication gap between workers and requesters, which consists of instructions, payment, time limit on task, and the interface for workers to work on. It remains an underdeveloped but important topic that needs further exploration for improving crowdsourcing experience. My research studies task delivery from requesters to crowd workers. The goal is to improve the communication between the two and, in turn, increase accuracy of results and decrease variability due to differing interpretations and perspectives. Specifically, this dissertation presents a series of studies to show that high-quality results can be obtained from human workers through improved task design, by 1) designing incentives to recruit workers with the appropriate skills for given tasks, 2) designing unambiguous instructions to clearly express task requirements, 3) choosing the correct strategy to communicate the requisite task knowledge with workers, and 4) enhancing requesters’ ability to rapidly prototype Augmented Reality (AR) instructions. This dissertation demonstrates that crowdsourcing quality is improved when the tasks are communicated using mediums and structures that align with workers’ preference and utility