Applying prompts to GenAI/LLMs

One of the most popular uses of Generative AI is summarising documents and information sources. Now for the but, could it be even better? Adams et (2023) the issues of using ChatGPT4 to produce the right amount of information in a summary. Balancing the detail in the summary without it becoming too dense, it is hard to follow, whilst ensuring it is relevant to the source. In the paper they investigated applying a technique they called "Chain of Density" (COD) to produce a better summary compared to a standard "summarise is this" prompt.   Chain of Density in Summary Start with the standard summarise this kind of prompt - fairly non-specific. 1. Identify one to three more informative entities from the article 2. Write a new denser summary of the same length including all the entities identified (don't remove entities when identified) expanding the detail Repeat steps 1 and 2 around 5 times. Applying guidelines around improving on the previous summary but must be ...

Two recent papers Eliot, L. (2023) and White, J.  et al.  (2023) have listed strategies for prompt engineering.  Taking Eliot, L (2023) paper categories different types of prompts to get different actions or outputs. The key point I feel is that LLMs are powerful tools that are a lot more than search tools, ie. "Tell me the answer to this". Selecting our prompts we can get LLMs to be closer to a tool for enhancing our creativity and productivity. Here's a table summarizing the prompt engineering strategies from Eliot (2023) and their summaries: Strategy Summary Imperfect Prompting Using intentionally imperfect prompts to generate creative or unexpected outcomes. Persistent Context and Custom Instructions Prompting Setting a persistent context or providing custom instructions to tailor AI responses. Multi-Persona Prompting Directing AI to adopt one or multiple personas for role-play or perspective exploration. Chain-of-Thought (CoT) Prompting Requesting AI to process ...