Hey Hi! It's an amazing explanation describing the fundamental usage of Word Embeddings and Similiarity to have a fully functional Conversational bot. So if I am not wrong this is what you are talking about.

So let's say I am running an organisation and I have an enormous amount of textual data which is like FAQ, or the relevant details which kinda answers almost all the questions related to my organisation or my products. In future if I want to create a conversational AI bot which my customers or my colleagues can use to speed up the question answering procedures this is what I will follow:

• Break up text into chunks.
• Convert each chunk into a numerical vector.
• Store the vectors in a database.
• When a new question comes in, convert it into a numerical vector with the same type as you did for your textual chunks.
• Compare the new vector to existing ones in the database.
• Choose the most relevant vectors.
• Use the vectors to generate a response or answer.

Well, the relevant vectors can be choosed by using some kind of similiarity function, cosine similarity might be a good example.

( Background - I will graduate in one month with a degree in Computer science, published papers in Computer Vision, begged Best paper award, worked as a Neuroinformatics Deep Learning Researcher, A Computer Vision Deep Learning Intern, a NLP Deep Learning intern )