Use cases for large language models (LLMs) have grown significantly over recent years - from providing basic customer service to writing code and scripts and even creating content such as blogs and songs.
Indeed, Christoph Börner, Senior Director of Digital at Cyara, explains how he leveraged a LLM for inspiration when his band were struggling to come up with material for a new song.
"Within a few seconds, it had an intro, chorus, verses and a solo," he noted.
Börner describes his surprise at how good the output was, noting how - with just a small bit of polishing and tweaking - they were able to include it in their set list.
Such capabilities of LLMs – such as GPT, PaLM and Falcon – have led to deployments of conversational AI skyrocketing across numerous industries and all stages of the customer journey.
For instance, in the analysis stage of customer journey building, organizations utilizing LLMs to promptly generate relevant customer contact reasons and queries is an exciting new use case.
Meanwhile, in the design phase, LLM applications have the capability to conduct the entire dialog management including conversation flows, lexicons, and even "personas" – which allow the bot to interact with customers in a specific style and manner.
As a final example, consider the training phase. There, a technician tasked with making sure a customer-facing bot can understand and respond to customers appropriately is able to use LLMs to auto-generate new and more appropriate training data for the bot.
Cyara, a company focused on supporting organizations in assuring and optimizing their entire customer experience (CX) environments including conversational AI channels, is at the forefront of several such use cases. Noting the latter, Börner said:
“We released an AI Data Wizard within Cyara Botium, which leverages a LLM to spot undertrained intents (the goal that a user has within the context of their conversation with a chatbot), and then – with a press of a button – generates new training phrases.”
Solutions like that enable brands to bring bots to the market quicker and - in the case of many dream deployments – come equipped with new, flashy features.
Examples of Dream Deployment Already in Place
First up, UK bank NatWest leveled up its virtual agent - “Cora” – with generative AI (GenAI), so it is able to answer particular customer questions without prior training.
Now known as Cora+, the bot plugs into trusted, secure, business-specific knowledge sources to send responses in a “natural, conversational style”.
Meanwhile, Cora+ also cites the source material for each of its responses, so customers can dive deeper into it if they wish.
Next, consider fashion retailer GAP, which implemented a similar solution, leveraging domain- and industry-specific language models.
In doing so, GAP claims an 84 percent auto-resolution rate, up from 50 percent before the GenAI augmentation and extending far beyond its target of 70 percent.
A final and excellent example is Pelago, a travel experience platform established by Singapore Airlines Group, which layered GenAI over its existing conversational flows.
As such, its bots can adjust their responses to the changing context of the conversation, resulting in more “personalized, near-human planning experiences” – as per Yellow.ai, Pelago’s tech partner.
Interestingly, in this example – and likely many others – Pelago has actually leveraged multiple LLMs to achieve the desired results.
