Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

Onsite system design round at Anthropic for a software engineer role. The interviewer was unusually warm and actually typed notes into the shared doc while I talked so I could stay focused on thinking, which was a nice change from the usual awkward silence while you wait for someone to catch up.

Questions Asked (1)

Design a prompt playground (a tool that lets users write, test, and iterate on prompts for a language model).

System DesignProduct Sense & IdeationTechnical Trade-offs

Author's notes

The interviewer kept typing as I talked, which sounds small but actually changed how I thought through the problem.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the core user needs and scope of the prompt playground, then outline a high-level architecture covering the frontend, backend, and model integration. Dive into key technical challenges such as real-time streaming, versioning, and evaluation, and discuss trade-offs between simplicity and advanced features. Conclude by tying your design back to Anthropic's mission of safe and beneficial AI.

Pro tip: Emphasize the importance of a tight feedback loop for prompt iteration, and propose features like side-by-side model comparison and automatic prompt versioning to demonstrate product thinking beyond basic functionality.

1. Clarify Requirements and Scope

Ask questions to understand target users (e.g., developers, researchers), key use cases (e.g., testing, debugging, collaboration), and constraints (e.g., supported models, latency, cost). Define MVP features versus future enhancements.

2. High-Level Architecture

Sketch the system components: a web-based frontend for prompt editing and result visualization, a backend service to manage prompts and orchestrate model calls, and integration with Anthropic's API. Consider data storage for prompts, versions, and results.

3. Core Features and User Experience

Detail essential features: prompt editor with syntax highlighting, parameter controls (temperature, max tokens), real-time streaming of model responses, and side-by-side comparison of different prompts or models. Include versioning and history.

4. Technical Challenges and Trade-offs

Discuss challenges like handling concurrent requests, streaming responses efficiently, storing and retrieving large volumes of prompt/response data, and ensuring security (e.g., API key management, user isolation). Trade-offs: simplicity vs. advanced features, cost vs. performance.

5. Evaluation and Iteration

Propose mechanisms for evaluating prompt quality, such as automated metrics (e.g., BLEU, perplexity) or human feedback. Suggest A/B testing and integration with CI/CD pipelines for prompt deployment.

Key Points to Mention

Real-time streaming of model outputs to reduce perceived latency and improve user experience.
Prompt versioning and history to track changes and enable rollback.
Side-by-side comparison of prompts or models to facilitate iteration.
Parameter controls (temperature, top-p, max tokens) for fine-tuning model behavior.
Security and access control: API key management, user authentication, and data isolation.
Scalability considerations: handling multiple concurrent users and large prompt histories.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.