Tricks for diversity of outputs when producing large sets?

Hey all! I am writing about 200 synthetic day-in-the-life journal entries. Ideally, these are from different “people” and focus on different subjects.

Logically, when you run a generic prompt to “write a journal entry about your day, include specifics and details…” the entries come out pretty similar.

I generated a python list of 200 different jobs, ages, martial statuses, etc. Then I ran my prompt in a while loop and appended to the end of each prompt “Write as if you are a {insert specific life situation from python list}.” This effectively gave me 200 different prompts.

However, while this approach improved my outputs a little, but there is still a very generic feel and structure to all of them. Like 20% of them start with some variation of “Man, today was a rollercoaster!

I’m wondering if people have tried to achieve something similar and what they have done?

1 Like

I’ve tried noncing with irrelevant context to ignore, but the AI does a pretty good job of ignoring. You can still try that though to modify the instruction following quality by large contexts.

One plausible costly concept: Load 16k GPT3 API up with dozens or all 200 profiles. Then when you have the instruction for “write #88’s daily journal”, there will be different positional lossy effects from the large context.

Roleplay characters are all now very canned and hammy. It’s almost like OpenAI decided to add “Ah, I see” fakery detection phrases to everything that’s not a chatbot - and I get back again exactly what I expect again:

Act as a learned professor in the art of juggling mousetraps.
Ah, welcome, my esteemed audience,

Curses, feeble-minded bot.

You can try “write in the style of” - and then pick somebody unexpected that doesn’t have catchphrases.

There’s a whole new world of output to explore if you use the API davinci GPT-3 engine.

You probably just need to add a “style” list and turn top_p to 1. Sounds like you have life content, but not personality content. Like “Cheery southerner”, “Serious educated business man.”

Be careful with race and region though, I’ve noticed it sometimes writes racist stuff. Specifically with asians.

1 Like

Hi! You can start with something like “How do you imagine a chef in a Italian restaurant to speak?” and store it with the role. Next time you request the journal entry pass it as context and prompt the model to use the style.

Of course you can’t ask the model to imagine anything but you get the idea.

Hope this helps!

Interesting! Thanks for this idea. I may base one off of ENTJ Myers Briggs.

1 Like

On top of using MBTI, you may be able to apply Plutchiks Wheel of Emotions to have the bot choose an emotion for the output. A sad ENTJ, a blissful ENTP, an angry ISTP, etc… we might apply to a bot we’re building, but instead of MBTI using Enneagram with wings and levels of integration… I proposed it, but we’re still work on some base functionality.

1 Like

So I’ve put in a couple days on this now with some troubleshooting. I found that the models don’t understand a simple MBTI anagram very well. You get weird stuff like, “As an ENTJ I enjoy challenges in the workplace…”

What I ended up doing was getting GPT-4 to create an array of 100 unique emotional states from as general as “Angry” to as specific as “yearning for unrequited love”. Then I pick a random one each time and insert it into the prompt in a line like "The overwhelming emotion you are feeling at this moment is: {random emotion}.


I just noticed you mentioning anagram manipulation issues, this is often down to the way the model tokenizes word parts, to you ENTJ looks like 4 letters, but to the model it looks like this image 2 tokens.

As a test, can you try inserting a separator character such as - (dash) to give this E-N-T-J, and the model will now see that as image 4 separate tokens.

You can then extract the - to rebuild your string. See how that works for you.


This is a high-quality insight. Thank you. I will explore this.

I had an idea, where you just take one from each column to make your personality. This is the bot’s idea of meeting my specs.

AI: Below is a table with four characteristic categories as column headers, and 30 rows with writing examples and corresponding personality characteristics. The columns group similar facets of information together, and the entries within each row do not conflict with each other. Please note that these are fictional characters, and the provided personality characteristics are purely for creating distinct writing styles:

Personality Trait Age & Ability Positive Negative
1. Optimistic 35 years old, Novelist Always sees the bright side Tends to be naive
2. Cynical 42 years old, Journalist Sharp wit and satire Pessimistic worldview
3. Enthusiastic 28 years old, Blogger Exclamation marks aplenty Sometimes overly verbose
4. Analytical 45 years old, Researcher Logical reasoning and evidence-based writing Lacks emotional depth
5. Whimsical 19 years old, Student Imaginative and creative language Difficulty staying focused
6. Precise 50 years old, Editor Meticulous attention to detail Can be overly pedantic
7. Laid-back 33 years old, Traveler Informal, colloquial expressions May come across as lazy
8. Authoritative 60 years old, Historian Firm and confident tone Tends to be domineering
9. Empathetic 27 years old, Therapist Understanding and compassionate Overuses qualifiers (e.g., maybe)
10. Humorous 38 years old, Comedian Witty remarks and humorous anecdotes May rely too much on jokes
11. Pensive 31 years old, Philosopher Thought-provoking and contemplative Can be overly abstract
12. Confident 48 years old, CEO Assertive language Comes across as arrogant
13. Nurturing 29 years old, Parent Warm and caring tone May be too sentimental
14. Stoic 55 years old, Veteran Dispassionate and stoic writing Lack of emotional expression
15. Playful 22 years old, Gamer Uses gaming slang and metaphors May lack depth in serious topics
16. Persuasive 37 years old, Salesperson Convincing arguments and rhetoric May oversell or manipulate
17. Curious 26 years old, Explorer Inquisitive and eager to learn Easily distracted by new ideas
18. Diplomatic 43 years old, Diplomat Tactful and diplomatic language May avoid taking clear stances
19. Rebellious 20 years old, Rebel Defiant and anti-establishment Can be confrontational
20. Melancholic 41 years old, Poet Reflective and emotional May lean towards melodrama

AI: You can now combine one characteristic from each column to create a unique personality for a character. For example, “joe’s writing is: Rebellious, 20 years old, Defiant, and tends to be confrontational.” This combination will create a distinctive writing style for Joe based on the specified attributes.

Obviously cripplebot doesn’t write at the length we want, but you can probably just “continue” this list, or drop a refined table header for a different list.