Practical Prompt Engineering. Tips and tricks for successful… | by Cameron R. Wolfe, Ph.D. | Jul, 2023

Tips and tricks for successful prompting with LLMs…

(Photo by Jan Kahánek on Unsplash)

Due to their text-to-text format, large language models (LLMs) are capable of solving a wide variety of tasks with a single model. Such a capability was originally demonstrated via zero and few-shot learning with models like GPT-2 and GPT-3 [5, 6]. When fine-tuned to align with human preferences and instructions, however, LLMs become even more compelling, enabling popular generative applications such as coding assistants, information-seeking dialogue agents, and chat-based search experiences.

Due to the applications that they make possible, LLMs have seen a quick rise to fame both in research communities and popular culture. During this rise, we have also witnessed the development of a new, complementary field: prompt engineering. At a high-level, LLMs operate by i) taking text (i.e., a prompt) as input and ii) producing textual output from which we can extract something useful (e.g., a classification, summarization, translation, etc.). The flexibility of this approach is beneficial. At the same time, however, we must determine how to properly construct out input prompt such that the LLM has the best chance of generating the desired output.

Prompt engineering is an empirical science that studies how different prompting strategies can be use to optimize LLM performance. Although a variety of approaches exist, we will spend this overview building an understanding of the general mechanics of prompting, as well as a few fundamental (but incredibly effective!) prompting techniques like zero/few-shot learning and instruction prompting. Along the way, we will learn practical tricks and takeaways that can immediately be adopted to become a more effective prompt engineer and LLM practitioner.

(created by author)

Understanding LLMs. Due to its focus upon prompting, this overview will not explain the history or mechanics of language models. To gain a better general understanding of language models (which is an important prerequisite for deeply understanding prompting), I’ve written a variety of overviews that are available. These overviews are listed below (in order of…

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