Microsoft has acknowledged a common frustration faced by AI users across industries: well-intended prompts often fail to produce the expected results, forcing users into a repetitive cycle of rewording and retrying. This back-and-forth not only wastes time but also reduces the overall value of AI tools that are meant to enhance productivity, especially for knowledge workers who rely on AI to understand, explain, or clarify complex information.
According to Microsoft, this trial-and-error approach can feel unpredictable and discouraging. Instead of helping users learn faster or work smarter, AI interactions sometimes demand more effort in crafting the “right” prompt than in actually using the response. To address this challenge, Microsoft has introduced a new open-source UI framework called Promptions, a combination of “prompt” and “options,” designed to bring structure, clarity, and consistency to how people interact with large language models.
Understanding the real bottleneck in AI usage
While public discussions around AI often focus on content generation such as writing or image creation, a significant portion of enterprise AI usage revolves around comprehension. Employees frequently ask AI systems to explain concepts, debug issues, or teach workflows. In these cases, the quality of understanding matters more than creative output.
Take a simple example like a spreadsheet formula. One user might need a brief explanation of syntax, another may want help troubleshooting an error, while someone else might want a step-by-step explanation suitable for training colleagues. Although the input appears similar, the intent behind each request is different. Traditional chat-based AI interfaces struggle to capture these nuances, leaving users to manually explain context through long and carefully worded prompts.
Microsoft notes that clarifying intent often requires extra effort, making AI interactions tiring rather than intuitive. This gap between what users want and what AI understands is the core problem Promptions aims to solve.
How Promptions changes the way users interact with AI
Promptions works as a middleware layer that sits between the user and the AI model. Instead of relying solely on free-form text prompts, the system dynamically generates interface controls based on the user’s input and conversation history. These controls allow users to specify preferences such as explanation depth, tone, learning objective, or response format without rewriting their entire prompt.
By offering clickable and adjustable options in real time, Promptions helps users communicate intent more clearly. This approach shifts the interaction model from “prompt engineering,” where users must carefully craft language, to “prompt selection,” where intent is guided through structured choices. As a result, AI responses become more aligned with user expectations and more consistent across different users within an organisation.
Balancing efficiency with usability
Microsoft researchers tested Promptions by comparing static interface controls with its new dynamic system. The results showed that participants found it easier to express task-specific requirements using dynamic options rather than repeatedly rephrasing prompts. This reduced mental load allowed users to focus more on understanding the information provided by AI rather than managing how they asked for it.
However, the tests also revealed important trade-offs. While adaptability was appreciated, some users found the system harder to interpret at first. The impact of selecting certain options was not always immediately clear, and users sometimes had to see the output before understanding how a control influenced the response. This learning curve highlights the challenge of designing dynamic AI interfaces that are both powerful and transparent.
Inside the Promptions architecture
From a technical perspective, Promptions is designed to be lightweight and easy to integrate. Its architecture is built around two core components. The Option Module reviews the user’s prompt and prior conversation to generate relevant interface elements, while the Chat Module uses the selected options to shape the AI’s final response. An important advantage for enterprises is that the system does not require storing data between sessions, keeping it stateless and reducing data governance and security concerns.
This design makes Promptions particularly appealing for organisations looking to standardise AI usage without adding heavy infrastructure or compliance complexity. By guiding intent through interface design rather than hidden prompt rules, teams can achieve more predictable outcomes from AI tools.
A design pattern rather than a final fix
Promptions is not positioned as a complete solution to all AI prompt-related issues. Instead, Microsoft presents it as a design pattern that organisations can experiment with inside internal tools, developer platforms, and support systems. When properly calibrated, such frameworks can reduce variability in AI outputs, improve user confidence, and enhance overall productivity.
At the same time, success depends on careful implementation. Too many controls can overwhelm users, while unclear option effects can reduce trust. For technology leaders, Promptions represents a practical step toward more reliable AI interactions, emphasizing thoughtful interface design as a key factor in making AI truly useful in everyday work.
With Promptions, Microsoft signals a shift in how AI systems may evolve in 2025 and beyond, moving away from purely conversational models toward guided, intent-aware workflows that better align AI capabilities with real human needs.
