Acceptance empowers creativity by allowing minds to explore new, uncharted territories. High achievers often disregard current constraints, fostering innovation. This enables artists and scientists to break traditional boundaries, uncovering unique ideas.

In this article, we discuss the only approach to unlocking your creativity whether that is for work, personal projects or just general life.

Mastering prompt engineering can elevate your creative projects to new heights. At Asycd, we offer ten crucial tips to refine your AI prompts, enhancing outputs for poetry, marketing copy, and more. From leveraging multiple AI systems to defining clear instructions, these tips will help you become a more productive generative AI user.

The RAG approach enhances an LLM's responses by providing additional context. This significantly improves relevancy and accuracy. However, one of the methods we've been delving into, the LLM-Assisted Multi Query RAG, does have its limitations. Primarily, it's quite token-intensive. Generating multiple queries through an LLM can consume a significant number of tokens, which could be a concern if you're working with a limited API usage budget.

To tackle this, we've been experimenting with different strategies. For instance, dynamic query generation, where the number of queries varies based on the initial input’s complexity, has shown some promise. We're also looking into optimizing embedding retrieval as a way to better manage token usage without sacrificing response quality.