Deploying Successful GenAI-based Chatbots with less Data and more Peace of Mind.

A Demo to Experience the Results

In this post we are going to see a few examples of the different types of answers we get. These examples are captured from the public demo we have published at https://www.bitext.com/sales-demos/

To compare the different answers we get from the three models, we will use a common question: “I want to open an account”.

Answer from GPT-3.5, the General Purpose Model

Answer from Pre-trained Banking Model

Since this Model is already verticalized for Banking, the Model safely assumes that the account we are asking about is a bank account and easily provides proper instructions on how to proceed.

In other words, the Model already knows the vocabulary and expressions from the Banking domain, and can solve semantic ambiguities that the generic model cannot, like what the meaning of “account” is in this request.

Additionally, the Model provides a specific style for the answer, following standard corporate rules for language like tone, vocabulary, sentence length… as it is common practice in customer support.

Answer from Client-Specific Customized Banking Model

Since this Model is already customized for a specific bank (in this case, the fictitious BBI bank), the Model provides customer-specific and correct instructions on how to proceed to open an account specifically in BBI.

This customized Model already knows not only the vocabulary and expressions in Banking but also the specifics of one particular bank. Additionally, the Model uses the particular tone and style of BBI, following its corporate communication rules.

Conclusion

As we have shown, customizing Large Language Models in 2 steps via fine-tuning is a very efficient way to reduce data needs, as well as training and evaluation efforts, when building customized Conversational Assistants. Bitext provides these Pre-Built Datasets and Models in 20 verticals. Some examples of data, models and demos can be found here