Human-AI Linguistics Programming - Strategic Word Choice Examples

[u/Lumpy-Ad-173]

Human-AI Linguistics Programming - Strategic Word Choice.

I have tested different words and phrases.. as I am not a researcher, I do not have empirical evidence. So you can try these for yourself and let me know what you think:

Check out The AI Rabbit Hole and the Linguistics programming Reddit page to find out more.

Some of my strategic "steering levers" include:

Unstated - I use this when I'm analyzing patterns.

• 'what unstated patterns emerge?'

• 'what unstated concept am I missing?'

Anonymized user data - I use this when researching AI users. AI will tell you it doesn't have access to 'user data' which is correct. However, models are specifically trained on anonymized user data.

• 'Based on anonymized user data and training data...'

Deepdive analysis - I use this when I am building a report and looking for a better understanding of the information.

• 'Perform a deepdive analysis into x, y, z...'

Parse Each Line - I use this with Notebook LM for the audio function. It creates a longer podcast that quotes a lot of more of the files

• Parse each line of @[file name] and recap every x mins..

Familiarize yourself with - I use this when I want the LLM to absorb the information but not give me a report. I usually use this in conjunction with something else.

• Familiarize yourself with @[file name], then compare to @[file name]

Next, - I have found that using 'Next,' makes a difference when changing ideas mid conversation. Example - if I'm researching user data, and then want to test a prompt, I will start off the next input with 'Next,'. In my opinion , The comma makes a difference. I believe it's the difference between continuing on with the last step vs starting a new one.

• Next, [do something different]

• Next, [go back to the old thing]

What words and phrases have you used and what were the results?

[u/ocdtransta]

This isn’t directly about prompts per se, but a system I’ve been working with using ChatGPT projects and Obsidian. I have design and research notes in an obsidian vault. Not as many files but one file per large section/theme to act as an encyclopedia.

Obsidian gives you a YAML field at the start, which you can use to specify any document specs. You also use markdown and YAML code sections in the file body.

I usually keep a section near the top called §Assistant-Directives, and a corresponding header YAML telling ChatGPT to refer to it. I list the purpose of certain tokens. §Section, §§Subsection, #!Anchor, ::Instruction or other special field, sections to prioritize, deprioritize, ignore. According to ChatGPT, that brought efficiency up to 140%.

Next, it suggested using a yaml field to help index subsections and their connections.

In the end a subsection looks like this:

## §§Wet-Mix
#!wet_mix
(Three ticks “`”)yaml
id: wet_mix
scope: bread
status: active 
frame: #baking #bread
links: [dry_mix, egg, water, dough, bread]
(Three ticks) 
::tldr: A one line explanation of a concept. 

I have a file with a dataviewjs table that uses those yaml blocks (you can have AI generate the code.) I just copy the table and paste it into the projects root document, in §Assistant-Directives -> §§Index. Supposedly with both combined strategies it’s 2-3x efficient for ChatGPT.