Session 2: Single prompts, conversational prompts and personas – 90 minutes

Let’s consider the prompts we used to generate the second two poems in the previous section. Why did they give more control over the poem that was created?

In both cases, we gave a specific topic the poem should be about (speed), we suggested elements that needed to be included in the poem (train journeys, car races, galloping horses).

We identified the specific audiences we wanted the poem to be read by and some characteristics of that audience that might make the poem more or less relevant (10–14-year-olds, English speakers living in a big city and music students learning about song writing).

Finally, we gave it some parameters to work with – we wanted four verses in both, with the first having a strong rhythm, and in the second we gave it a style we wanted it to copy. It seems to have met those criteria fairly well – although it might be hard to determine what it did with the information that they lived in a big city. Such is the nature of GenAI: just because the prompt contains something, it does not mean the AI makes explicit use of it.

Prompt frameworks

Unsurprisingly, there has been a lot of research and opinions on what elements combine to make the best prompts (collectively known as prompt engineering). This has resulted in a wide range of prompting frameworks.

They all try to give guidance on how best to frame the task by supplying information, examples, descriptions or expectations, and asking the AI to check and evaluate what the AI should do before presenting an answer. Helpfully, many of the frameworks all come with a catchy mnemonic.

Let’s consider the similarities and differences between two prompt frameworks: CRISP and CLEAR.