Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly crafted "perfect prompt" for a task. To mitigate the high degree of effort, we instead ask whether collecting multiple decent, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed method, Ask Me Anything (AMA). We first develop an understanding of the effective prompt formats, finding question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. True or False?"). AMA recursively uses the LLM to transform task inputs to the effective QA format. AM generates multiple questions per input and applies these prompts to collect several noisy "votes" for the input's true label. We find the prompts have varying accuracies and dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions. We evaluate AMA across open-source model families (EleutherAI, BLOOM, OPT, and T0) and sizes (125M-175B parameters), demonstrating an average performance lift of 10.2\% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting.