Perspectives for first principles prompt engineering

Prompt engineering is the process of designing and optimizing prompts to elicit responses from large language models (LLMs) that meet user expectations for relevance and quality. The basic process involves framing the problem, writing an initial prompt, optimizing it according to the model's strengths and weaknesses, identifying errors, and iterating. Best practices include using the best model available, clearly separating instructions from context, being specific and concise, and employing few-shot prompting. The article discusses three theoretical perspectives on LLMs: as stochastic parrots, flexible dynamical systems, and pseudocognitive artifacts. The stochastic parrot perspective views LLMs as retrieval mechanisms that regurgitate pretraining data, while the flexible dynamical systems perspective emphasizes their ability to interpret and extrapolate beyond mere repetition. The pseudocognitive artifact perspective likens LLMs to human reasoning, suggesting that they simulate human linguistic thought based on their training data. Each perspective offers insights into how to effectively engineer prompts, emphasizing the importance of understanding the model's capabilities and limitations, as well as the context and intent behind the prompts.

- Prompt engineering optimizes prompts to align LLM responses with user expectations.

- Best practices include clarity, specificity, and leveraging the model's strengths.

- LLMs can be viewed as stochastic parrots, flexible systems, or pseudocognitive artifacts.

- Understanding LLMs' capabilities aids in crafting effective prompts.

- Iterative refinement is crucial for improving prompt effectiveness.