Right before LLMs broke into the scene we had a few techniques I was aware of: * Personality Forge uses a rules-based scripting approach [0]. This is basically

Right before LLMs broke into the scene we had a few techniques I was aware of: * 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-chatbots.php https://www.personalityforge.com/developers/how-to-build-cha... [1] https://web.archive.org/web/20200104080459/https://rasa.com/ 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). [2] https://github.com/RasaHQ/rasa https://github.com/RasaHQ/rasa [3] https://rhasspy.readthedocs.io/en/latest/ https://rhasspy.readthedocs.io/en/latest/