* Personality Forge uses a rules-based scripting approach [0]. This is basically ELIZA extended to take advantage of modern processing power.
* Rasa [1] used traditional NLP/NLU techniques and small-model ML to match intents and parse user requests. This is the same kind of tooling that Google/Alexa historically used, just without the voice layer and with more effort to keep the context in mind.
Rasa is actually open source [2], so you can poke around the internals to see how it's implemented. It doesn't look like it's changed architecture substantially since the pre-LLM days. Rhasspy [3] (also open source) uses similar techniques but in the voice assistant space rather than as a full chatbot.
[0] https://www.personalityforge.com/developers/how-to-build-cha...
[1] https://web.archive.org/web/20200104080459/https://rasa.com/ (old link because Rasa's marketing today is ambiguous about whether they're adding LLMs now).
If I remember correctly, I also modified the Graphmaster to add support for rule priorities, so that I can better manage rules beyond the tree-based matching approach.
One of the first things people would do, upon discovering that she's a bot, is trying to break her responses.
All of this was for private use, nothing was open sourced. Unfortunately I think I forgot to copy it over from an old hard drive during a computer hardware migration, so it's gone now.
I remember Richard Wallace writing something along the lines of "if I were to build an artificial intelligence, I wouldn't use flesh and bones, that's just a bad choice" (not a verbatim quote) in defense of people accusing AIML for being a too simple/dumb of an approach, with those people favoring more complex approaches. In the age of LLM, that statement aged both well and badly.
There were. If you're really interested in that history, one place to look is at the historical record of the Loebner Prize[1] competition. The Loebner was a competition based on a "Turing Test" like setup and was held annually up until 2019 or so. I think they quit holding it once LLM's came along, probably because they felt like LLM's sort of obviated the need for it or something.