Generative AI Prompt Builder
Node-based Gen-AI prompt building platform; currently part of SAP Business AI and SAP Build
Challenge
Text-based tasks take too long time, but there is no perfect solution to cater each situation
Opportunity
Develop a Gen-AI Promp Builder that allows to orchestrate multiple ML (machine learning) models and use any Generative AI LLMs (large language models) for business application usage
Team
So Yeon Kim (Me, UXD)
Nassim Bahloul (UXD)
SAP ICN US Team (PM, Developers)
Timeline
4 Months
Tool
Figma
Microsoft Teams
MIcrosoft Words
Task
UX Design
UI Design
Prototype
Background
Creating a Gen-AI platform for everyone, not just developers
Business communications generate vast unstructured data—emails, texts, notes, and call transcripts. Manually reviewing and responding is time-consuming for any user.
The project started as a quick prototype for SAP employees to write emails easily by connecting different machine learning models, but it lacked organized screens and navigation.
Another UX designer and I joined to make the platform more intuitive. We focused on creating clear interactions relations and screen designs for the alpha release.
Product Structure
Integrating multiple models for efficient text analysis
The platform has two key functions: 1) integrating various LLM models and 2) using them for text-based business tasks like email summarization.
Chat GPT
Cohere
Claude
SAP LLM
Inhouse LLMs
Etc
Customize multiple LLMs
in a 'Package' format
Build prompts leveraging 'Packages'
Email analysis
Document analysis
Chat analysis
Response generation
Automatic CS
Etc
Solution
Configuration: Connecting different LLM and ML models
Connect multiple models in 'Package'
Users can customize a package of language models on the Configuration page
Node-based 'Package' visualization
To intuitively show what models are connected, I designed the platform in the node layout
Prompt Builder: Utilizing 'Package' to analyze texts
Changing parameter
Users can add, customize, and delete each parameter
Auto and customized prompt building
Users can apply the settings to auto-create a prompt formula while also adding their own prompt by typing
Security check
The user can turn on/off the security check, which will examine whether the prompt will cause any issues; the service will provide the user with a suggestion when hovering on the detected issue
Running prompt to get the outcome
Users can provide text and run the prompt to see the output preview
Prompt Lobby & Store: Sharing Packages with colleagues
Lobby: A personalized library to view my prompts and templates
Used tag components to indicate prompt properties like LLM type and prompt usage
Store: a place to browse and add public prompts
Located a browsing feature with advanced filtering options
Reflection
Learning Gen-AI to Design for Gen-AI
Understanding the back-end was essential for creating a logical design. Unlike simply using Gen-AI outputs (e.g., ChatGPT responses), Communication Intelligence enables users to build and customize ML models and prompts.
Negotiable and Dynamic Screen Components
Managing large amounts of information was a challenge. A negotiable layout allowed personalization based on user needs, while dynamic components optimized space by adapting to scroll input—such as headers that fold automatically.
Visualizing ML Models with Nodes
Inspired by my creative background in Touch Designer and Unity Shader, I introduced a node-based interface. This approach allows users to visually connect elements, review their workflow, and better understand the impact of each selection.