Design a Prompt Playground
Design a system for a "prompt playground" catering to developers and prompt engineers. This platform enables users to craft prompts, configure AI model settings, execute prompts against various AI models, and stream results in real-time back to the browser.
Key functionalities include:
- Saving different versions of prompts.
- Comparing outputs from various runs.
- Facilitating collaboration among teammates.
- Providing tools to inspect cost, latency, and safety aspects of prompt executions.
Scale and Constraints
Unless explicitly stated otherwise, assume the following operational scale:
- Monthly Active Users (MAU): 100,000
- Prompt Runs: 1 million per day
- Real-time Streaming: A subset of prompt runs requires real-time token streaming to the browser.
- Organizational Structure: Users are organized into workspaces or organizations.
- Data Sensitivity: Both prompt content and AI model outputs are considered sensitive data.
Design Scope
Your design should comprehensively cover the following areas:
- Core user flows and functional requirements.
- Definition of APIs and the underlying data model.
- High-level architectural overview.
- Detailed mechanisms for prompt execution, result streaming, retry logic, and cancellation.
- Implementation of versioning, collaboration features, and maintaining run history.
- Strategies for security, privacy, rate limiting, and abuse prevention.
- Approaches to observability, cost tracking, and discussions on scalability tradeoffs.
AI interview prep
Core dilemma
Orchestrating high-volume, real-time interactions with external, potentially slow and expensive AI models, while maintaining robust state (versioning, history, collaboration) and stringent security/privacy for sensitive data.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a Prompt Playground
Design a system for a "prompt playground" catering to developers and prompt engineers. This platform enables users to craft prompts, configure AI model settings, execute prompts against various AI models, and stream results in real-time back to the browser.
Key functionalities include:
- Saving different versions of prompts.
- Comparing outputs from various runs.
- Facilitating collaboration among teammates.
- Providing tools to inspect cost, latency, and safety aspects of prompt executions.
Scale and Constraints
Unless explicitly stated otherwise, assume the following operational scale:
- Monthly Active Users (MAU): 100,000
- Prompt Runs: 1 million per day
- Real-time Streaming: A subset of prompt runs requires real-time token streaming to the browser.
- Organizational Structure: Users are organized into workspaces or organizations.
- Data Sensitivity: Both prompt content and AI model outputs are considered sensitive data.
Design Scope
Your design should comprehensively cover the following areas:
- Core user flows and functional requirements.
- Definition of APIs and the underlying data model.
- High-level architectural overview.
- Detailed mechanisms for prompt execution, result streaming, retry logic, and cancellation.
- Implementation of versioning, collaboration features, and maintaining run history.
- Strategies for security, privacy, rate limiting, and abuse prevention.
- Approaches to observability, cost tracking, and discussions on scalability tradeoffs.