How to improve UX in AI, part 3: Integrations and Documentation

Although Artificial Intelligence (AI) and Large Language Models (LLMs) are hot topics in the business world, hardly anyone is talking about user experience (UX) in AI.

Nevertheless, these are the end users that mainly benefit from or struggle with the AI models as they use them on a daily basis. Therefore, their experience should be prioritized when designing AI chatbots.

To highlight the matter, we have started a miniseries on the blog with specific hands-on solutions to the most common issues when working with LLMs. The first part, where we discussed improving searching capabilities, can be found here, and the second, where we covered one-click actions, can be found here.

In this article, we will handle the third and final lever of UX in AI, which are integrations and documentation. Let’s dive in.

Why are integrations important for UX in AI?

In a nutshell, integrations refer to connections between AI systems and other software, enabling them to work together efficiently. Integrations are beneficial both for individual users and companies, as they:

• • streamline and optimize workflows, for example, AI models integrated with Power Automate can trigger actions based on predictive analysis,

• • enhance data usage as AI systems can access datasets and, as a result, provide more accurate insights and predictions,

• • improve accessibility by facilitating interaction between AI systems and individuals for non-technical users,

• • enhance scalability and automation capabilities as they can leverage the infrastructure and capabilities of multiple platforms.

Summing up, integrations of various systems with Large Language Models can optimize processes within companies, allowing individuals to easily access and analyze data or improve accessibility.

How can generating documentation increase UX in AI?

On the other hand, we have documentation capabilities, which are also needed in AI models.

If you have ever worked in a company where there are many processes or procedures, you must be aware of the importance of documentation.

It serves as a guide for users, helping them understand, implement, and troubleshoot systems. Unfortunately, hardly anyone is keen on creating thorough documentation. And here, we could use LLMs.

If we take into consideration the levers we covered in the series, which are:

1. 1. searching capabilities
   2. one-click actions, 

We could use a standardized one-click “Documentation” button to seamlessly create documentation based on the data sources quoted by AI and a predefined format, for example, a one-page pdf.

What is more, if we add integration to it, we could also enhance the documentation with data coming directly from the main sources within businesses or companies, for example, system logs from CRM, ERP, or event logs.

In fact, current LLMs are great at summarizing texts. Adding the possibility to create documentation with one click could hugely impact UX in AI and streamline the heavily important but also tedious process.

All in all, UX in AI bridges the gap between advanced technology and end users’ needs. Enhancing current AI models with better searching capabilities, the possibility to define one-click actions, integrations with internal systems, and documentation generation could completely transform the user experience when using LLMs and take companies and businesses to the next level.

We hope that you have enjoyed the series on UX in AI. If you have any comments or additional questions, don’t hesitate to contact us through the contact form on the blog.