How Generative AI brings efficiency to customer support
Let’s face it. Customer support is like the hydra of product-development tasks. With every new service or feature introduced, a multitude of frequently asked questions (FAQs) emerge. As your product scales and evolves, your support efforts must also rise to meet the demands of maintaining a personalized approach and upholding your golden standard of customer service. Fortunately, there's a powerful tool at your disposal: Generative AI.
Let's examine recent developments in Large Language Models (LLMs). These models can supercharge your efforts by creating a fluent language interface. They address FAQs and provide personalised support, ensuring your customer support experience doesn't feel robotic or limited.
From Q&A to personal information
What makes Large Language Models (LLMs) a game-changer? Well, the key distinction lies in their flexibility when it comes to language understanding, setting them apart from traditional NLP techniques. LLMs excel in comprehending existing context and understanding the customer's intent. This allows for the creation of a personalised experience by linking only the relevant pieces of the puzzle together.
The beauty of LLMs is that you don't have to predefine an answer for every conceivable user question. The trick is to give Generative AI access to an already existing knowledge base. The AI then combines the question with the appropriate context to carry on the conversation seamlessly.
So how can you provide access to these knowledge bases? Based on a question or conversation with a user, we can decide which existing snippet of information from the existing knowledge base is relevant to inject into the conversation. This approach saves computational resources that would otherwise be needed for retraining an LLM. Instead, the existing model can ingest the required information and form a coherent answer.
Putting it into practice
Still with us? Let’s clarify with an in-house example. At In The Pocket, we implemented this technique with our own employee journey documentation. Instead of bombarding our Office Team with repetitive questions that already have documented answers somewhere, our colleagues can now turn to ChatITP for assistance. One particularly neat feature is that ChatITP not only provides an answer but also includes a source. This allows users to validate the answer and, if desired, explore further reading material.
A more personalised experience
But why stop there when you can elevate this experience to a whole new level of personalisation? In situations where questions are highly context-specific, it's beneficial to not only incorporate an existing knowledge base but also leverage some data from the user's profile. For instance, in the case of a retailer, this could involve considering the items in the user's shopping cart, while a utility company might take into account device types or family composition. Importantly, you don't even have to include the actual user's data. Thanks to the language comprehension abilities of modern LLMs, a transformed version of the data is sufficient to generate a tailored response.
By granting the model access to external tools, you can, for example, provide information about whether the user is a minor without revealing their actual age. This way, the model possesses enough knowledge to carry on the conversation, while the user remains protected from unwanted exposure. Ultimately, you retain full control over data access, ensuring privacy and security.
Not a silver bullet, but still valuable
In conclusion, the integration of customer support services with Generative AI proves to be a powerful combination. However, it's important to acknowledge that this solution may not instantly resolve all your support-related challenges. It does, however, represent a significant advancement beyond a simple FAQ page.
By leveraging this technology, you can optimise the performance of your support agents and empower users to find answers themselves, thus accelerating the support process. This approach enables us to provide more personalised assistance to users. At In The Pocket, we are eagerly anticipating the future possibilities that this innovative solution will bring.