Predictive UI: Structuring LLM Outputs for Greater User Control

Abstract

Large Language Models (LLMs) have revolutionized human-computer interaction by enabling natural language communication. However, users often struggle with the variability of outputs, leading to a lack of control. This paper introduces Predictive UI, a novel approach that enhances user agency by structuring LLM-generated responses into selectable, customizable options. By predicting user intent and generating predefined modification categories, Predictive UI bridges the gap between natural language interfaces and structured control mechanisms, improving usability and satisfaction.

1. Introduction

The integration of LLMs into chat-based interfaces has transformed digital interactions. Users can now issue commands in natural language without the rigid syntax of command-line interfaces. However, unlike traditional interfaces with predictable outputs, LLMs generate variable responses, causing unpredictability and reducing user confidence in system behavior.

Predictive UI aims to address this challenge by offering users greater control over AI-generated content. By detecting intent and structuring output modification options, this approach provides a more stable and interactive experience.

2. Related Work

2.1 Natural Language Interfaces

Previous research has explored natural language as an intuitive mode of interaction, from early command-line systems to modern conversational AI. Despite advancements, the lack of predictable outputs remains a challenge.

2.2 Interactive AI Customization

Work on AI-assisted writing tools (e.g., ChatGPT, Grok) has focused on improving output relevance. Some tools allow manual refinements, but few offer structured predictive controls.

3. Predictive UI: Concept and Design

3.1 Definition

Predictive UI is an interaction model that detects user intent and pre-generates structured modification options to refine LLM outputs. Rather than receiving an unpredictable response, users interact with a UI that suggests categorized refinements.

3.2 Example Scenario

Consider a user prompting an LLM:

“Write me a caption to announce the release of X.”

A standard LLM might generate a single caption, varying each time. Predictive UI, however, would:

1. Detect intent: Recognize that the user seeks a caption.

2. Generate structured options: Provide customization parameters such as:

• Tone of voice: (e.g., Formal, Casual, Exciting, Professional)

• Length: (e.g., Short, Medium, Long, Concise)

• Style variations: (e.g., List format, Narrative, Question-based)

The user interacts through a selection interface, refining the output without re-prompting the model.

4. Implementation and System Architecture

4.1 Intent Recognition

A lightweight intent classifier detects key elements of user prompts. This can be implemented using a fine-tuned LLM or heuristic-based rule sets.

4.2 Predefined Modification Categories

A structured database of modification categories maps to common use cases, ensuring consistency across different prompts.

4.3 UI Design

Predictive UI integrates selectable options through a minimal, intuitive interface, allowing real-time refinements before finalizing outputs.

5. User Experience Benefits

5.1 Increased Control

Predictive UI mitigates randomness, providing users with predefined options to guide the AI’s output.

5.2 Reduced Iteration Time

By structuring responses, users achieve desired results faster without repetitive re-prompting.

5.3 Enhanced Predictability

Providing categorized choices reduces the unpredictability of AI-generated content, increasing user confidence.

6. Conclusion

Predictive UI represents a step toward controlled, structured LLM interactions that balance the benefits of natural language interfaces with user agency.