Generative AI Alone Can't Solve Your Customer Support Issues

Customer service leaders have long wished for a true virtual assistant to help customers and partners get accurate answers without calling the helpdesk, but is this nirvana actually attainable? With the advent of generative artificial intelligence models like ChatGPT, there is hope that these technologies can finally handle customer inquiries and complaints. As a result, a new form of search technology called Answer Engines has been gaining favor among customer service, customer engagement, and content/knowledge management professionals.

Answer engines enable users to ask questions as if they were asking a subject matter expert and get correct answers regardless of how they phrase their questions. Generative AI solutions alone can produce realistic-sounding responses, but they cannot ensure the accuracy of their answers and cannot be considered an actual answer engine.

Consequently, customer service leaders have learned that they can't rely on generative AI alone to create a real answer engine. Elements, such as a memory that understands company and domain-specific information and a retrieval system that substantially narrows inputs, must be added to create a solution that can actually deliver a fully functional virtual assistant. Fortunately, all these components now exist and enable organizations to create effective virtual customer support agents that can answer questions accurately and instantly.

ChatGPT and other generative AI models have made significant strides in mimicking human-like conversation. Responses are generated based on their training data and an interpretation of vast amounts of text data/learning patterns to predict appropriate replies. However, this approach inherently cannot verify the accuracy of the information the models generate. Unlike humans, AI models lack real-world experience and contextual understanding, and the quality of their output is heavily dependent on their training data. They might provide responses that sound plausible but are factually incorrect.

Relying solely on generative AI for customer support has led to misinformation that frustrates customers and employees alike and can result in damaging outcomes. Although dozens of vendors have attached generative AI to their tools, they can't deliver accurate answers without a memory and the ability to limit input to relevant information, regardless of how compelling the answers sound.

To formulate appropriate and accurate responses, generative AI models require constrained inputs. Extraneous and irrelevant input will confuse them and result in inaccurate answers. For example, using generative AI to summarize a lengthy list of accurate and inaccurate results will only yield a poor summary answer. Worse, it can't verify or correct it.

The Role of an Answer Engine

To address these limitations, answer engines act as the gatekeeper of information for AI models. Answer engines can generate direct, accurate, and trusted answers that are anchored on accurate and relevant information from reliable sources. They act as a filter, ensuring that the input provided to generative AI is limited to only trustworthy and contextually relevant information. One clue to whether your organization is using an answer engine is the result of a query in the search engine prior to results being fed to a generative AI tool. If the query returns hundreds or thousands of responses that require the user to sift through pages of results, it's likely that the replies are coming from a search engine and not an answer engine.

By leveraging natural language processing and machine learning, answer engines can understand customer queries, comprehend the intent, and find appropriate answers from trusted knowledge sources. They feed only a handful of relevant results to the generative AI model, ensuring that the generated responses are grounded in reliable information.

Answer engines combine the strengths of generative AI, large language models (LLMs), embeddings, and vector and semantic data representations on the same platform to deliver high-quality output. They can pre-process customer queries, analyze the intent, and extract the necessary information to generate a response. By limiting the input to relevant and reliable sources, answer engines significantly reduce the chances of misinformation or incorrect answers.

Furthermore, they allow for monitoring and continuous improvement of generative AI's performance. By comparing generated responses against known accurate answers, answer engines can evaluate the accuracy and quality of the output. This feedback loop helps identify areas of improvement, allows users to fine-tune AI models, and increases reliability over time.

Robust Analytics from an Answer Engine

One of the key benefits of answer engines is more detailed analytics to identify missing, inaccurate, or duplicative content. They also validate whether end users receive good answers and generate new content based on trusted information that can be reviewed by content creators. Over time, this will produce an even more robust and efficient content set to address end user queries without the need to call the helpdesk.

While generative AI models have made remarkable advancements in simulating human-like conversation, their limitations in accuracy assurance and the dependency on a reliable and constrained source of input make them ill-suited for enterprise deployment by themselves. However, by integrating generative AI into an answer engine, organizations can leverage their combined strengths to provide accurate, relevant, and trustworthy customer support. That vastly improves their customer support efforts and creates a customer support ecosystem that balances automation with human oversight, leading to improved customer satisfaction and trust.

Ryan Welsh is founder and CEO of Kyndi.