Why not ChatGPT?

For businesses wanting to leverage AI in their business, a public large-language model (LLM) like ChatGPT is often the first thing that comes to mind.

For many of us, ChatGPT has been the first entry-point into playing with what generative AI can do.

However there are some big issues presented when a business wants to take the power of ChatGPT into their business, perhaps to help staff or customers answer questions, or to automate workflows.

One of the most obvious limitations is that, while ChatGPT knows a lot, it knows very little about your internal knowledge base, which is where the real gold is.

“ChatGPT was trained using text databases from the internet. This included a whopping 570GB of data obtained from books, web texts,”

As mind boggling as it is to consider how much data large language models like ChatGPT have been trained on, this is mostly what's on the internet at large.

If a customer asks a specific question around your services, products, or knowledge, they’ll likely receive a generic or inaccurate response.

Consider for example how misleading that would be for an insurance company that wants a virtual agent to answer specific questions about a customer's policy?

Then we have another big fear for businesses: security. Not too many businesses are too happy about opening up their internal data to a third-party public AI model.

RAG to the Rescue

An emerging solution is in the AI world is known as RAG: retrieval-augmented generation.

RAG combines the power of a large language model (like Open AI's ChatGPT, Anrhtopic's Claude, or Google's Gemini) with a custom retrieval module that taps your own organisation's knowledge base(s).

The 3 biggest benefits of a custom RAG solution are:

1. Customisation for Unique Business Knowledge: A RAG model ensures accurate and relevant responses, because it's fine-tuned on a business's knowledge base which could include company policies, research, product information and more.
2. Data Security and Privacy: With ChatGPT, your company's data has to leave your premises and systems to be processed by a third-party service. This raises potential security and privacy concerns, especially for businesses dealing with sensitive information. A custom RAG solution hosted within your own infrastructure keeps all data in-house for tight security.
3. Scaling and Cost Management While convenient, the public ChatGPT has usage limits and scaling challenges. Building your own RAG allows scaling compute resources as needed while controlling costs based on your actual requirements.

Making RAG and Gen AI Accessible

Building a production-grade RAG solution is not a trivial undertaking. It requires data engineering to create queryable knowledge bases, model training expertise, and infrastructure to host and serve the model.

At Custom D we’ve created Caitlyn a productised service that provides organisations with a custom RAG solution for a fraction of the cost that it would take to setup from scratch. It can be trained on your data, customised and integrated into your website or applications so that it’s a seamless part of the experience offered by your business.

In essence: Caitlyn takes the power of a large language model (like ChatGPT) and adds security, privacy, and hyper-relevant responses based on your company’s unique knowledge and the customers' needs.

Get in touch if you'd like a demo and to learn more about how it works.