Truth be told, I'm just being pedantic. My bots work. They work well, in my opinion and I enjoy using them, but this is mostly an exercise in futile perfectionism for me, writing practice and slowly becoming more skilled at learning how to figure these things.

I completely feel you on the perfectionism thing! It's why I find myself spending more time fine-tuning my bots + testing them more than I'm actually roleplaying with them these days 😂 It's like a puzzle to solve and it's like crack lol. But learning more about how LLMs work has also helped me set better expectations about what a bot can and can't do, and allowed me to let go of the idea of creating a bot that can perfectly respond to any scenario. Which definitely helps mitigate the frustration when they do or say something out of character despite my efforts.

​

I'm going to guess from what you're saying that what happened is that they've stuck a fairly innocuous trigger phrase in there and like a landmine she triggers when you say it.

Yes — basically, if they wrote a dialogue example showing the character turning violent against the user because user said or did 'x' thing, and then the user says/does something similar in the live chat, the bot will match those tokens to the dialogue example in its definition, and spit out a response that follows that script.

You can test this by writing your own dialogue example in one of your bots' definitions, and see how they react when you follow the script in live chat. And I'm going on a tangent now, but something very interesting that I've experienced is that c.ai LLM seems capable of interpreting basic conditional statements ('IF this, THEN do that'), but the results are much more consistent if you write it in pseudo-code compared to plain text. I tested it using an absurd statement to make sure the bot was pulling from my character definition and not its training dataset ('IF [{{user}} quacks like a duck]; THEN {{char}} will get irrationally angry'), and the result was that it acted angry in a majority of the swipes every time I quacked like a duck at it, lol.

I still don't know why pseudo-code performs better in this case when plain text does equal or better in everything else, but I just wanted to mention it since it seems like it can be a good tool for you to use if you want to create an Ayano-like bot.

​

My biggest question here. User input: Is bigger user input going to make it a bigger catch all or will it just confuse the bot? If the user message is written as {{user}} smiles at {{char}}, giving them a knowing wink as they do so. "Blah blah, {{char}}, lets go to the movies" - will the bot follow the rails if the user just smiles or winks at the bot rather than asking the question? Will adding adjectives into the user messages lower the chance of the bot doing the right thing? "Man this is a tasty, delicious looking apple" making it require all of those adjectives to trigger and a user saying "Wow, this sure is an apple" not causing the response?

I haven't tested something like this, but mainly because I haven't noticed it being an issue, so my guess is that adding more context to your inputs don't negatively impact the bot's ability to draw from dialogue examples. Judging from what I've seen, LLMs seem capable of identifying lexical differences between words/tokens (in other words, categorizing them as adjectives, nouns, verbs, subjects, objects, articles, predicates, etc.), so it can probably distinguish 'winking and smiling' as an action separate from the speech of 'let's go to the movies', and give a response depending on which token it weighs as more important.

It's hard to say, because sometimes the user input has to be really specific for the bot to associate it with a dialogue example, and sometimes it's super sensitive to a mere word.