The recent surge in the development of attention mechanisms has made it possible for language models (LMs) to produce text on par with humans. Unfortunately, the autoregressive nature of attention-based LM decoders inhibits them from attending to future information. In this paper, we first define the task of future conditioning: the process of motivating an LM decoder to condition on a future event. We then propose the Future Sight story generation pipeline for fine-tuning an attention-based LM decoder on the future conditioning task. We evaluate Future Sight against three datasets—Writing Prompts, ROCStories, and RecipeNLG. We measure the utility of Future Sight on text inter-diversity and entailment against a fine-tuned GPT-2 baseline. Both our novel automated entailment evaluator and human readers found texts generated by Future Sight to more sensibly lead up to a future event. Additionally, we observe a decrease in inter-diversity for batches of predictions with a fixed future event, demonstrating positive conditioning effects of the future. Our intention for the Future Sight pipeline is as a human-in-the-loop system where authors can propose their own future plot events to guide an LM as it drafts a story.