This blog post discusses the author's exploration of using output from a semantically distant analogy generator, named RAMS, to create synthetic data for training large language models (LLMs). The author shares insights into their innovative approach, which involves an updated retrieval logic to generate high-quality analogies from unrelated fields, and invites feedback from the community on the potential utility of this method in overcoming challenges in LLM training.