Is Your AI Chatbot Making Your Property Look Uninformed?

Hey there, welcome to Day 10 of "21 Days of AI"!

Get ready to meet RAG—Retrieval-Augmented Generation, which makes your Multifamily AI tools super accurate and trustworthy.

Today's Highlights:

• What is RAG, and how does it make AI factual?
• How does RAG sharpen Multifamily AI?

Ensuring that prospects and residents receive accurate and reliable information is paramount in the multifamily industry. This is where Retrieval-Augmented Generation (RAG) steps in.

LLMs Without RAG

Multifamily AI tools powered by Large Language Models (LLMs) like GPT-4 are incredibly powerful, capable of generating human-like text and understanding a wide range of contexts. However, these tools have a significant limitation: they cannot reliably store or recall factual information.

Instead, they rely on "stochastic knowledge," which means their responses are generated based on patterns and probabilities rather than solid facts. This often leads to non-factual or ambiguous answers.

For example, a property using an AI-powered Chatbot might know that people love to swim in summer, which is common knowledge. However, whether their community pool is open right now is a factual detail. The Chatbot cannot provide accurate information to the residents of that community without access to specific, up-to-date facts.

What is RAG: LLMs + Facts = RAG

Retrieval-Augmented Generation (RAG) combines the generative power of Language Learning Models (LLMs) with real-time factual data to produce accurate and reliable responses. RAG systems augment LLMs with a continuously updated database of verified facts, ensuring the information is always current and accurate.

When a prospect asks a question to an AI-powered Chatbot, the LLM generates a response that is cross-referenced with this fact database, ensuring the answer is contextually appropriate and factually correct. The fact database can be updated regularly. For multifamily properties, this means that details about amenities, availability, pricing, and policies can be kept up-to-date, allowing AI tools to provide precise and trustworthy information.

How RAG Works: A Multifamily AI Chatbot Example

Imagine you're managing a multifamily property, and a prospect asks your AI chatbot, "Can you tell me about the neighborhood around your community?" By leveraging RAG, the chatbot can provide precise and factual responses.

Here's exactly how RAG helps:

1. Understanding the Question: The chatbot first understands that the prospect wants information about the neighborhood around the property.
2. Finding Information: The chatbot then searches your property's database or documents, such as neighborhood guides, local listings, and reviews, for relevant information about the area.
3. Getting the Right Details: It pulls out specific details about the neighborhood, such as nearby schools, parks, restaurants, shopping centers, and public transport options.
4. Responding to the prospect: The chatbot creates a clear and informative response using the details it found. For example, it might say, "Our community is in a vibrant neighborhood with excellent schools like Green Valley Elementary, parks like Maple Park, and popular restaurants such as Bella's Bistro. Nearby shopping includes Macy's and Whole Foods, with the Main Street bus stop just two blocks away."

Using RAG, the chatbot ensures it provides precise and comprehensive answers about the neighborhood, helping prospects get a better sense of the area and making it easier for them to decide.

In conclusion, the equation is simple but powerful: LLM + FACTS = RAG. This combination transforms AI from a general knowledge tool into a precise, reliable assistant that handles complex and varied inquiries typical in multifamily operations.

And that’s a wrap on Day 7 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!