You are considering building a chatbot. If you decide to build an artificial intelligence (AI) powered chatbot you will undoubtingly question yourself the following: which NLU engine should I use?
Natural Language Understanding (NLU) is often treated as a black box, but understanding how it works is crucial for building effective conversational AI solutions.
NLU is the component of conversational AI that interprets human language and converts it into structured data that machines can process. It’s the foundation that enables chatbots to understand user intent and extract relevant information.
Different NLU engines have different strengths and weaknesses. Key factors to consider include:
Successful NLU implementation requires:
Understanding NLU is essential for building effective conversational AI. By demystifying this black box, businesses can make informed decisions about their chatbot architecture and achieve better results.
In my previous article about AI, I mentioned how everyone suddenly seems to be an AI expert and how that creates a lot of noise. In this post, I'll break down one of the most important topics: how (and why) you should add your company's data to large language models.
Let's be honest: I think it's great that technology is so embedded in our daily lives. It helps us get knowledge faster, complete tasks more efficiently, gives us inspiration, and occasionally scares the hell out of us. But after spending a ridiculous amount of time with these technologies, I feel it's time to reflect on what I really hate about AI.
Someone once told me, stupid questions don't exist. This might be true, but people are still afraid to look stupid by asking questions that are assumed to be general knowledge. In this article, my goal is to get everyone up-to-speed on the basics of AI.
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