This post talks about the accordion effect that can happen while users are interacting with chatbots and copilots.
Right now, chatbots have taken over when it comes to Gen AI tools for researching and finding answers from text input. Every time you talk to AI, it's like a simple trade: you say something, AI says something back. This trade between prompts and responses can often bog users down.
UX researchers and experts in the field have noticed something interesting: people often go back and forth with AI, tweaking what it spits out with more questions or prompts. This back-and-forth with the text-only interface leads to a common behavior:
The Accordion Effect can increase depending on what type of response your users are looking for. If your users are asking simple questions to which they want simple and concise answers, they will most likely want "Low-Fidelity Output". For example, the content takes priority and requires a simple straight-forward answer. If your audience is seeking help with generating content by providing elements such as tone and writing style, your chatbot will want to respond with "High-fidelity Output". User seeking answers to questions typically prefer low-fidelity output but even then, can result in information overload.
Images have the unique ability to communicate complex ideas and concepts through multiple visual elements like color, shape, and layout all at once. However, text is limited to a one-dimensional, linear format. This limitation becomes especially apparent when users need to find specific answers to detailed questions. Sifting through long blocks of text can be a time-consuming and inefficient, as users must read sequentially, potentially missing crucial information or getting bogged down in irrelevant details.
Training chatbots with simple, concise data enables rapid retrieval of relevant answers to complex questions, enhancing user experience through direct responses. It optimizes chatbot performance for fast, accurate query processing. Leveraging well-structured, simple data allows organizations to streamline information access via chatbots, empowering users to navigate intricate inquiries efficiently without having to scan lengthy text.
Often, LLMs will reference a dataset and combine information from multiple sources. If the prompt input is not specific enough the user will have to "shrink" or "expand" the response to narrow or expound on the information. This shrinking and expanding is why we refer to it as the "Accordion Effect". Here is an example:
With Fiber Copilot, we've recognized the need to reduce this accordion effect by implementing key features into our enterprise chatbot technology that organizations can train with custom datasets. For example, to reduce the need for shrinking or expanding with multiple prompts in a chatbot conversation, our AI recognizes patterns to follow up responses with questions or hints to further refine responses without typing lengthy return prompts. Our UX practitioners are constantly looking for ways to improve the efficiency of how Fiber delivers information so that users can accomplish tasks at a higher rate and volume. Talk to a Fiber Copilot AI expert today to see how this process and features works.
Schedule a demo with our experts and learn how you can pass all the repetitive tasks to Fiber Copilot AI Assistants and allow your team to focus on what matter to the business.
