Customer service leaders across federal, state, and local government agencies want to provide each citizen with the absolute best experience possible to citizens, similar to the experience that elected officials have with their staff who research questions on their behalf and simply provide them the answers. The emergence of generative AI models like ChatGPT have sparked hope that these large language models can finally address customer inquiries and complaints efficiently. However, it is crucial to acknowledge that generative AI has limitations with the accuracy of its responses, which prevents it from serving as a true, accurate “answer engine” behind popular self-service customer support tools like chatbots or virtual assistants.
The bottom line is that government agencies cannot rely solely on generative AI to create their virtual customer support agents. To build a fully functional solution, agencies need a true answer engine comprising several essential components, including generative AI, embeddings from language models, vector and semantic data representations, and analytics to ensure high quality output. The good news is that all these components now exist, making it possible to develop an effective virtual customer support agent capable of providing accurate, trusted and instantaneous answers. Let’s explore how these components can work together to overcome these limitations.
Why Generative AI Fails to Provide Accurate Answers
Generative AI models have made significant progress in mimicking human-like conversation. They can generate responses by interpreting vast amounts of text data and learning patterns from their training data to predict appropriate replies. However, their inherent limitation lies in their inability to verify the accuracy of the information they generate. Unlike humans, AI models lack real-world experience and contextual understanding, which can lead to plausible, yet factually incorrect, responses. Regardless of how compelling the answers sound, government agencies must understand that generative AI alone cannot deliver accurate responses without these essential components.
For generative AI models to produce appropriate and accurate responses, they require inputs that are relevant and accurate. Irrelevant or extraneous input confuses the AI and leads to inaccurate answers. Using generative AI to summarize a mix of accurate and inaccurate results, for instance, will only yield a poor summary answer without any validation. Without proper mechanisms to filter and validate input data, the result is incorrect or misleading customer support responses.
Generative AI as a Component of a True Answer Engine
When integrated into an answer engine, generative AI models can provide accurate and contextually relevant responses to citizens’ inquiries. An answer engine pre-processes the query, analyzes intent, and extracts necessary information for generating a response. By limiting input to relevant and reliable sources, an answer engine significantly reduces misinformation risks and incorrect answers. Additionally, an answer engine enables continuous improvement of the AI’s performance by comparing generated responses against known accurate answers and fine-tuning the AI model over time.
End Game — An Effective Self-service Customer Support Ecosystem
While generative AI models like ChatGPT have advanced significantly in simulating human-like conversation, they are not production-ready solutions for comprehensive customer support in government agencies. The limitations in accuracy assurance and dependency on reliable and constrained input necessitate the consideration of an answer engine. By integrating generative AI’s conversational capabilities with filtering and validation features of an answer engine, government agencies can develop an effective and intelligent self-service customer support ecosystem, much like the diligent staffer provides for each government official. At the end of the day, understanding the importance of accuracy assessment and sourcing reliable information ensures citizen satisfaction and builds trust in government services.
Ryan Welsh is the founder and CEO of Kyndi, a global provider of the Kyndi Generative AI-powered Answer Engine that provides users with direct, accurate, and trusted answers instantly. To learn more visit https://kyndi.com/ or follow them on LinkedIn and Twitter.
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