Poppy AI·AI Engineer·Take-home Assignment·Junior

JuniorPass

Aug 2026Remote

Summary

Applied for an AI Engineer role at Poppy AI and got one of the stranger interview experiences I've had as a new grad. The process involved a Loom recording, direct texts with a founder, and a take-home Claude wrapper project that ended with me getting accused of half-assing it for following their own README.

Questions Asked (1)

Build a Claude-based wrapper that generates YouTube scripts, including an optional third-party data integration.

API & IntegrationsTechnical Trade-offs

Author's notes

The README literally said the external data integration was optional and that cached data was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: script length, tone, target audience, and what the third-party data integration should provide (e.g., trending topics, keyword metrics). Then outline a modular architecture using Claude for generation, with a pluggable data layer that can be toggled on/off, and discuss trade-offs like latency, cost, and reliability.

Pro tip: Emphasize idempotency and caching for the third-party data calls to avoid redundant API costs and rate limits, and mention fallback strategies if the integration fails—this shows production maturity beyond just wiring APIs together.

1. Clarify Requirements and Scope

Ask about script length, tone, target platform, and what specific data the third-party integration should fetch (e.g., YouTube trends, SEO keywords). Confirm whether the integration is optional and how it should degrade gracefully.

2. Design the Architecture

Propose a modular pipeline: input handling, optional data enrichment, prompt construction, Claude API call, and output formatting. Use a feature flag or dependency injection to make the third-party integration optional.

3. Handle Data Integration and Prompt Engineering

Describe how to fetch and normalize third-party data, then inject it into the prompt as context. Discuss prompt design to ensure Claude uses the data effectively without hallucination.

4. Address Trade-offs and Reliability

Cover latency (parallelize data fetch with prompt prep), cost (cache data, limit token usage), and failure modes (retry, fallback to no-data mode). Mention rate limiting and error handling for both Claude and the third-party API.

5. Propose Evaluation and Iteration

Suggest metrics for script quality (e.g., engagement, coherence) and A/B testing with/without data integration. Outline how to monitor and improve the system over time.

Key Points to Mention

Modular architecture with optional data integration via feature flags or dependency injection
Prompt engineering techniques to incorporate third-party data without overwhelming the context window
Caching and idempotency for third-party API calls to reduce cost and latency
Graceful degradation: fallback to Claude-only generation if the integration fails
Trade-offs between real-time data freshness and API cost/rate limits
Evaluation metrics and A/B testing to validate the impact of the data integration

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