Why Multifamily Chatbots Fail Without Embeddings?

Hello and welcome to Day 5 of "21 Days of AI"!

Today, we're unraveling the magic behind AI embeddings—the secret codes that help AI understand and interact with human language.

What's in the post?

• What are embeddings, and how do they work?
• What Goes Wrong When Multifamily Chatbots Lack Embeddings?

What are Embeddings and Their Role in AI?

Embeddings in AI assign meanings to words, capturing not just the words themselves but also their relationships to other words. These meanings are represented as numerical values, enabling machines to understand and process complex nuances in human language.

Let's look at a popular example to understand how embeddings work:

This formula shows us that embeddings can capture and manipulate relationships between words in a way that reflects human understanding.

Here’s how it breaks down:

• "King" is linked to ideas of royalty and masculinity.
• By subtracting "man," you remove the masculine aspect from "king.”
• Adding "woman" brings a feminine touch to the mix.
• The result, "queen," merges the ideas of royalty and femininity, just as predicted by embeddings.

This transformation shows that embeddings do more than replace words; they analyze deeper contexts and connections. Thus, embeddings allow AI to recognize that both "king" and "queen" symbolize royalty but differ in gender, illustrating the importance of understanding context in language.

This capability of embeddings to handle such nuanced linguistic transformations makes them invaluable in AI applications, where understanding context and relationships within text is crucial.

The Importance of Embeddings in Multifamily Property Management

Now, let's explore how this technology plays a crucial role in enhancing communication and service in multifamily.

What Goes Wrong When Multifamily Chatbots Lack Embeddings?

Let's say you employ a chatbot to handle inquiries about your properties.

Without embeddings, this chatbot might easily mix up inquiries due to its inability to understand context. For instance, if a prospect asks about the "parking policy," the chatbot, focusing solely on the common keyword "policy," might mistakenly provide information about the "pet policy" instead.

This error occurs because traditional processing methods lack the capability to capture the deeper meaning of words and phrases. They simply scan for keywords without grasping the query's full context. As a result, without embeddings, the chatbot fails to differentiate between distinct topics like ‘parking’ and ‘pets’, leading to responses that don't address the prospect’s actual needs. This can frustrate the prospects and reflect poorly on your property’s management.

What Benefits Do Embeddings Bring to Multifamily Chatbots?

Consider another scenario where residents use different phrases to report the same issue via chatbot:

• "The AC stopped working."
• "My air conditioner is broken."
• "Need help with cooling system repair."

Embeddings help the AI recognize that these different phrases all refer to the same issue: air conditioning problems. It achieves this by converting the phrases into numerical vectors, which allows the AI to see the similarity in context and content among these different statements.

Consequently, the AI not only categorizes and directs maintenance requests accurately but also ensures reliable and prompt service responses, improving the overall resident experience regardless of how residents phrase their questions.

Embeddings do more than just cut down on errors; they expand the range of questions AI can handle and make responses more personal. This is especially important in multifamily property management, where prospects and residents might ask all sorts of different questions.

And that’s a wrap on Day 5 of "21 Days of AI!"

Thank you for reading, and see you in the next newsletter.

Feel free to let us know what AI topics you're curious about or want to understand better!