Combining an LLM and a Rules-based Chatbot

A rules-based chatbot allows for rapid development. Adding rules as you go, some of which can contain complex logic. Rules-based systems do not use large amounts of computation and especially don’t require GPUs, cheaper running costs and more responsive replies. As powerful as rules-based chatbots are, they also come with downsides. Creating rules is time-consuming and challenging to cover all the scenarios. The rules files quickly become quite large and cumbersome. Workarounds such as having a final catch-all rule can somewhat mitigate this.

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I’ve been working on chatbots on and off for a while now. The other day, I received a question about how I built the latest Chatbot.

“I love the Chatbot, and it has been something I’ve been trying to replicate. There is so much noise. I’m finding it hard to find a good solution. I’d be grateful if you could point me in any direction, tools or what to search for.” 

I had always intended to write about it. The scope may have expanded a little since then into a short series. This post covers some background on chatbots, a few principles for conversational interfaces and building a basic chatbot.

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