bit hyperbolic phrasing. deterministic layers in front of an llm makes sense. Also a form of prompt engineering in a way. Seem to see that regularly: better inputs to generate better outputs. At what point are you spending so much on inputs, you might as well make the output yourself that way? Is the assumption most companies can actually formulate their processes in terms of 'business logic'? What would an example look like?

don't iterate for corrections. Redo and iterate the instructions. This is a bit like prompt engineering the oracle, no? AI isn't the issue, it's your instructions. Up to a point, but in flux too.

Ha! n:: habit building of delegating, good point using precise language to teens as training for llms

great quote

https://web.archive.org/web/20240916043530/https://euansemple.blog/2024/09/13/bending-the-truth/

Euan Semple describes how he has to fill in an online appointment form for medical care because his doctor asked him to make an appointment, but none of the pre listed answer options match that case. It reads like #prompting #promptengineering as we do in #algogens You change the input because of a desired output, but the input itself is just that means and becomes meaningless in the process. Yet in this case that input is kept as 'truth' in a database, impacting #dataquality

https://web.archive.org/web/20230502113317/https://wattenberger.com/thoughts/boo-chatbots

This seem like a number of useful observations wrt interacting with LLM based tools, and how to prompt them. E.g. I've seen mention of prompt marketplaces where you can buy better prompts for your queries last week. Which reinforces some of the points here. Vgl [[Prompting skill in conversation and AI chat 20230301120740]] and [[Prompting valkuil instrumentaliseren conversatiepartner 20230301120937]]

https://web.archive.org/web/20230404050349/https://greshake.github.io/

This site goes with this paper <br /> https://doi.org/10.48550/arXiv.2302.12173

The screenshot shows a curious error which makes me a little bit suspicious: the reverse Axelendaer is not rednelexa, there's an a missing.

Interesting point by John Caswell. AI prompting is a skill to learn, can we simultaneously learn to prompt better in conversations with other people? Prompting is a key thing in collecting narrated experiences for instance. Or will more conscious prompting lead to instrumentalising your conversation partner? After all AI chat prompting is goal oriented manipulation, what to put int to get the desired output? In collecting narrated experiences the narrator's reality remains a focal point, and only patterns over collections of narrated experiences are abstracted away from the original conversations. n:: [[Prompting skill in conversation and AI chat 20230301120740]] n:: [[Prompting pitfall instrumentalising conversation partner 20230301120937]]

https://web.archive.org/web/20230301110422/https://www.grouppartners.online/blog/critical-conversations

The interface of Wordcraft sounds like some of that interface that note takers and thinkers in the tools for thought space would appreciate in their

Rather than pairing it with artificial intelligence and prompts for specific writing tasks, one might pair tools for though interfaces with specific thinking tasks related to elaboration and continuation. Examples of these might be gleaned from lists like Project Zero's thinking routines: https://pz.harvard.edu/thinking-routines

that sounds like a useful analogy. Prompting like you are the algo's improv partner. The flipside seems to be the impact the author himself is after: being prompted along new lines of inquiry, making the script your improv partner in return.

I take to the term prompt engineering. Designing prompts is important in narrative research, just as much as in AI, and in e.g. workshop settings. It's definitely a skill. Conversational prompts describes blog posts too.

This is intriguing, seeing a similar return on prompting GPT-3 as from blogging. After reading this essay the first time, I played with GPT-3 myself, and even from a first attempt it is clear what he means. It feels like a similar process, prompting GPT-3 and pushing a notion, bookmark or question into my blog's feed. The first reactions on both types bring similar levels of surprial. What is however missing from GPT-3 in comparison with my blog is that blog networks are more than a 1-on-1 prompt and respons. They form larger feedback loops, which in turn lifts signals above the noise.

The idea of prompting and supporting students is crucial to teaching. For prompting, a teacher might ask the whole class, calling on students raising their hands in the group or walking around to individual students to assess their knowledge.

For support, a teacher might provide a graphic organizer for a story and events, word banks, helpful hints, fill in the blanks, highlighting key words or verbal support with encouraging language. The obvious support would be reading the story aloud or to an individual student.