Advanced Prompt Engineering

The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. For example, we can prompt an LLM to reformat any document via a prompt similar to the one shown below. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/0*lMIZ4748GIlHZPvM.png" style="height:659px; width:700px" /> Using prompting to reformat an XML document (created by author) As demonstrated in the example above, the generic text-to-text format of LLMs makes it easy for us to solve a wide variety of problems. We first saw a glimpse of this potential with the proposal of <a href="https://cameronrwolfe.substack.com/p/language-model-scaling-laws-and-gpt" rel="noopener ugc nofollow" target="_blank">GPT-3</a> [18], showing that sufficiently-large language models can use <a href="https://cameronrwolfe.substack.com/i/117151147/few-shot-learning" rel="noopener ugc nofollow" target="_blank">few-shot learning</a> to solve many tasks with surprising accuracy. However, as the research surrounding LLMs progressed, we began to move beyond these basic (but still very effective!) prompting techniques like zero/few-shot learning. <a href="https://towardsdatascience.com/advanced-prompt-engineering-f07f9e55fe01">Visit Now</a>