Tapping into RAG: Creating a Tailored Advisor

Retrieval-Augmented Generation sounds like three made up words tied together to confuse them non techies. It stands tall among users who understand what machine learning and Artificial Intelligence is. And let’s be real! The term itself sounds a little pretentious but rest assured that it is not without meaning.

So, before we take a deep dive into understanding how to create a tailor-made advisor for yourself, you need to truly understand what RAG is in actuality. 

RAG is an AI framework and the best way to understand its architecture is to break it down into two parts; one is the retrieval end, and the other is the generative end. 

The retrieval end is like a library that you will feed with all the relevant documentation to creates cognizance in the system. The generative end on the other hand leverages the library at the backend to create a response to a query pertaining to that particular library.

 

Why Use RAG?

A generic LLM will spit out generic answers and what you need is the ability to harness the power of its neural network for a very specific database of information. So, for instance, your company provides a valuable service and instead of creating a list of frequently asked questions, allowing the user to ask their questions would open doors for more flexibility.

What you are essentially doing with RAG is creating machine learning solutions at a higher level. If you wanted to create your own advisor and train it to accuracy, you would have to go through several stages which would include creating your own nervous system, training it with data and then testing to check for accuracy, after which you would have to create a user interface. 

With RAG, you can easily leverage technology and readymade libraries in python to custom make your own Machine Learning platform.

A guide to RAG for Non-techies

1. Information Gathering: The most basic step is to gather information to build a knowledge base for your library. For that you must compile relevant documents, articles or databases you would like your library to pull from. This will create context for your advisor; when your advisor is queried, it will parse through all of the data that it is provided and not public data.
2. Smart Search Training: Once the library is set up the idea is to teach the retriever which documents to pull up when asked for a certain type of query.
3. Response Generation: Once the retriever pulls the required documents or information from the database, it creates an intelligent response for the person asking the query.

Applications for RAG powered Advisor

• Customer Support Chatbot

A customer support chatbot is one of the most superior services that can be created from a standpoint of customers. And the reason is that no one wants to go through documentation especially if your product is software because it’s like flipping pages back and forth until you get an answer. And more often than not, it’s hard to find a solid answer. 

A chatbot is a learned system that will generate answers to your specific questions without all the hassle. The convenience and efficiency makes it well worth it.

• Content Creation and Curation

Marketers are always responsible for highlighting the product, the company and the services and creating content around it. Hence, once the RAG model is trained on past content, its engagement rates and interactions, it has the ability to generate more such content with a vast knowledge base.

It takes out days’ worth of research and creates a competitive edge for the company marketing its products.

• Sales Support Product Advisory

Salespeople are almost always hand on with product knowledge but sometimes they need help too and especially the new ones. For example, if a potential customer asks about specific features or pricing, the advisor can pull up the most relevant details and help the salesperson provide a more persuasive pitch.

• HazenTech’s Attorney Advisor

HazenTech has created a web application that works as an advisor to attorneys. Our application reads documentation from medical service providers and insurance companies to identify the issue pertaining to Personal Injury Protection. It creates efficiency for the attorney’s by saving them time from perusing through several documentation. It drastically reduces the need for excessive resources and keeps the attorneys organized, which in turn boosts their top line and ROI.

Testing and Improving

The best thing about any Machine Learning model is that its accuracy increases over time. As you get feedback from users, you can refine what’s in your library and tweak the way the advisor responds to make it even more helpful.

Considerations

Since the RAG LLM is created for a specific purpose, there are certain considerations one has to keep in mind.

• Accuracy and Reliability of Data

Since all the responses rely heavily on the data provided, it is imperative that the documents, articles or data provided are as error free as possible. It’s crucial to ensure that the sources of information are accurate, reliable, and up to date. If this is not the case, then the responses may be error prone and unreliable to the user. Due to this, the advisor may not be able to cement trust with the user.

To combat this accuracy of the retrieved information needs to be verified before it is used in generating responses. Regularly update and audit the knowledge base to minimize the risk of spreading misinformation. At Hazentech, we have diligently ensured that latest data is uploaded with accuracy and further there is a review process where the attorney’s also mark the outcome of the advisor as accurate or inaccurate. Through this process, our application creates more effective responses helping to boost efficiency.

• Bias and Fairness

If the training data contains any bias, then the response will contain similar biases. It is entirely possible that the machine learning model will amplify those biases. For instance, if the data reflects gender, racial, or cultural biases, the model’s responses may also be biased.

For this, the data source needs to be diverse and revised overtime to separate any biases. Moreover, we can use fairness-aware algorithms to ensure that the impact of bias is reduced.

 

• Transparency

Users should understand how the RAG model arrives at its recommendations or advice. This includes being transparent about the sources of information and how they influence the generated content. To ensure this, there is a need for features that allow the users to view the source of information as well.

 

In conclusion, the implementation of a RAG based model has great potential for several different industries. By combining the strengths of retrieval and generative models, RAG enables a more informed and accurate response mechanism, enhancing customer engagement, content creation, sales support, and more. HazenTech employs the latest technology to create a remarkable application that serves its client with pointed accuracy and we continue to improve upon it overtime. Our application was created in such a way that ensures scalability and efficiency for our clients. We believe that by keeping in mind the considerations, we can deliver meaningful experiences to the user.