What is Retrieval Augmented Generation (RAG)?

Hi, I’m Danielle Turner, Welcome to the Property AI Tools newsletter where I share weekly deep dives into AI topics, the latest AI tools and news, all specifically for real estate.

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Today I’ll be exploring:

• What is RAG and how does it work?

• Why is it important?

• Use case examples

• My tool recommendations

What is RAG and how does it work?

Retrieval augmented generation is a framework for improving the accuracy and reliability of genAI models with information and data from relevant data sources such as documents, reference photos and the wider internet.

It combines the benefits of traditional databases with the power of large language models (LLMs) like ChatGPT and Gemini to produce outputs that are more accurate, current and relevant.

As part of this process, you can add your own proprietary data as a source, supplementing the LLM with specialised industry, company or use case knowledge.

An example of this would be creating a private AI assistant for your real estate team . To ensure your AI has tailored knowledge to help your users, you can upload FAQs, listings, website data and more so answers to your most important questions are available and accurate.

Why is RAG Important?

RAG provides some key benefits to users of GenAI systems including:

Access to More Information

Supplementing an LLM that every other company may already be using with your own IP enhances the validity and accuracy of it’s answers. Bare in mind, LLMs such as ChatGPT and Claude are limited to their training data, some of which may be outdated.

Providing up-to-date, relevant data is therefore necessary to reduce the likelihood of your AI assistant hallucinating and making up information.

Building Trustworthy systems

Having reliable internal sources that can be cited is a huge benefit of RAG. Whether your GenAI system is being used as an internal tool or customer facing resource, your data can be easily referenced as the source of information.

Governance

In the case of an AI systems audit, you will easily be able to provide a log of sources and information that has been used to train your GenAI system, especially when users are prompting and asking questions specifically about your company.

When can RAG be used?

It can be implemented into AI Agents, Chatbots and GenAI systems essentially enabling you to have conversations with your data and creating endless new uses for exploring, compiling and interacting with your data.

Your data is what differentiates your business from others who are also building AI applications.

Example Use Cases

1. Internal HR Assistant = A AI assistant supplemented with company specific data about HR, employee rights and company policy. With direct access to current internal resources, users can the RAG enhanced assistant to generate manuals, knowledge bases and onboarding materials.

2. Lead Research Agent = An autonomous AI agent that takes information from new sales enquiries, researches the property, local area and uses RAG to find similar property sales comps within your company. It generates a one page report for agents to take to client meetings to close new business.

How Can You Implement RAG?

As a starting point, I recommend exploring the following AI tools that help you to combine the power of large language models with your own data to build new, personalized experiences.

Chipp
Chipp is a no-code platform that enables you to build and deploy AI agents, saving your team hours each week. Create custom AI agents to solve real business problems quickly and efficiently. Their simple interface is great for beginners and supports multiple data source formats including url, PDF, CSV and even direct sharepoint integration. Chipp is GDPR compliant and ideal for both internal and customer facing solutions.

Landbot
Build and deploy chatbots with Landbot's versatile builder. Create personalised customer experiences to capture leads, engage customers and answer enquiries without the need for human intervention. No technical skills required.

Chatbase
For a more intermediate option with granular controls over conversational flows choose this option. It plugs in seamlessly with all your existing comms, support and calendar tools and can escalate conversations to a human agent when necessary. Ideal for multi-step, customer facing solutions.

Conclusion

The future of AI is in the data and the new agentic AI capabilities that can be created with that data. Now you have a better understanding of how these complex AI systems operate behind the scenes, what will you create?

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Thanks for reading!

Danielle 
Property AI Tools Founder | AI Consultant @ Caique