This thing sprawls in every direction and I did not pace myself well.
Start by clarifying requirements and scale (e.g., number of new episodes per day, latency targets, recap length). Then walk through the end-to-end pipeline: ingestion, transcription, summarization, serving, and monitoring, highlighting key design choices and trade-offs at each stage. Emphasize how you would evaluate and iterate on the ML components.
Pro tip: Anchor your design around the user experience: the recap should be timely, accurate, and engaging. Discuss how you'd measure success (e.g., listen-through rate, user retention) and use that to drive technical decisions like model selection and latency budgets.
Ask questions to understand the volume of new episodes, desired latency (e.g., recap available within minutes or hours), recap length, and supported languages. Identify non-functional requirements like cost, scalability, and fault tolerance.
Outline how new episodes are detected (e.g., via RSS feeds or a message queue) and how audio is fetched and stored. Choose a transcription approach (e.g., pre-trained ASR like Whisper, or a cloud service) and discuss handling of long audio, speaker diarization, and language detection.
Describe the summarization pipeline: preprocess transcript (e.g., segment, clean), apply a summarization model (e.g., fine-tuned LLM or abstractive model), and post-process for coherence and length. Discuss trade-offs between extractive and abstractive methods, and how to ensure factual consistency.
Explain how recaps are stored (e.g., in a database or object store) and served to users (e.g., via API, push notifications, or in-app). Consider caching, CDN, and personalization (e.g., different recap lengths per user).
Define monitoring for pipeline health (e.g., latency, error rates) and model performance (e.g., transcription WER, summarization ROUGE, user engagement metrics). Include A/B testing and feedback loops for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.