Microsoft ‘Promptions’ fix AI prompts failing to deliver

Microsoft believes it has a solution to AI prompts being given, missing mark response, and cycle repetition.

This inefficiency constitutes a drain on resources. “The trial-and-error cycle can feel unpredictable and discouraging, turning what should be a productivity booster into a time-waste. Knowledge workers often spend more time managing the interaction itself than understanding the material they were hoping to learn.

Microsoft has released Promptions (Prompt + Options), a UI framework designed to address this friction by replacing ambiguous natural language requests with precise, dynamic interface controls. The open source tool provides a way to standardize how your workforce interacts with large language models (LLMs), moving away from unstructured chat toward guided, reliable workflows.

Bottleneck in understanding

Public interest is often focused on AI producing text or images, but a large part of enterprise use involves understanding, i.e. asking AI to explain, demonstrate or teach. This distinction is vital for internal tools.

Consider a spreadsheet format: one user might want a simple syntax breakdown, another a debugging guide, and another a convenient explanation of peer teaching. The same formula can require completely different interpretations depending on the user’s role, experience, and goals.

Existing chat interfaces rarely capture this intent effectively. Users often find that the way they formulate a question does not match the level of detail that the AI ​​needs. “Clarifying what they really want can require lengthy, carefully worded, and difficult-to-produce prompts,” Microsoft explains.

Claims act as an intermediary layer to fix this familiar problem with AI claims. Instead of forcing users to type lengthy specifications, the system analyzes intent and conversation history to create clickable options — such as caption length, tone, or specific areas of focus — in real time.

Efficiency versus complexity

Microsoft researchers tested this approach by comparing static controls to the new dynamic system. The results provide a realistic view of how these tools work in a live environment.

Participants consistently reported that the dynamic controls made it easier to articulate the details of their tasks without repeatedly rephrasing their prompts. This reduced agile engineering effort and allowed users to focus more on understanding the content rather than managing the drafting mechanics. By displaying options such as “Learning Objective” and “Response Format,” the system prompted participants to think more intentionally about their goals.

However, adoption brings trade-offs. Participants appreciated the adaptability but also found the system more difficult to explain. Some had difficulty predicting how a specific option would influence a response, noting that the controls seemed ambiguous because the effect did not become clear until after the output appeared.

This highlights the balance to achieve. Dynamic interfaces can simplify complex tasks but may introduce a learning curve as the communication between the checkbox and the final output requires user adaptation.

Claims: The solution to fixing AI claims?

Requests are designed to be lightweight, acting as an intermediary layer between the user and the underlying language model.

The structure consists of two basic components:

• Option unit: Reviews the user’s conversation history and prompts to create relevant UI elements.

• Chat module: Combines these selections to produce an AI response.

Of particular note to security teams, “there is no need to store data between sessions, which makes implementation simple.” This stateless design alleviates data management concerns typically associated with complex AI overlays.

Moving from “agile engineering” to “agile selection” provides a path to more consistent AI deliverables across the enterprise. By implementing UI frameworks that guide user intent, technology leaders can reduce the variability of AI responses and improve workforce efficiency.

Success depends on calibration. Usability challenges remain regarding how dynamic options affect AI output and managing the complexity of multiple controls. Leaders should view this not as a complete solution to fix AI claims scores, but as a design pattern to test within internal developer platforms and support tools.

See also: Confusion: AI agents take over complex enterprise tasks

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