Inside the Archive: The Tech Behind Your 2025 Wrapped Highlights | Spotify Engineering
Spotify’s 2025 Wrapped Archive identified up to five remarkable listening days for each eligible user and turned them into personalized, LLM-generated stories. A distributed pipeline, carefully designed prompts, model distillation, and massive-scale pre-generation made it possible to create roughly 1.4 billion reports before launch. The system prioritized factual grounding, creative consistency, safety, and reliable parallel storage. ## Identifying Remarkable Listening Days - Spotify used a priority-ordered set of heuristics to evaluate each user’s full year of listening. - Straightforward categories included: - Biggest Music Listening Day - Biggest Podcast Listening Day - Biggest Discovery Day, based on first-time artists - Biggest Top Artist Day - Biggest Top Genre Day - More nuanced categories detected: - Nostalgic listening and throwback-heavy sessions - Unusual listening patterns that differed from a user’s typical taste - Contextual dates such as birthdays and New Year’s Day - Candidate days were ranked by narrative potential and statistical strength, reducing hundreds of millions of events to as many as five standout days per user. - A distributed data pipeline aggregated the results and stored listening data in object storage. - Messaging queues then moved each user’s data asynchronously into report generation. ## Prompt Engineering for Reliable Stories - Spotify spent more than three months iterating on prompts and evaluating edge cases. - The system prompt established: - Traceability to real listening behavior - A witty, sincere, and quietly playful tone - Safety constraints excluding references to drugs, alcohol, sex, violence, and offensive language - User prompts supplied: - Detailed daily listening logs - Precomputed statistics, since LLMs are unreliable at arithmetic - Overall Wrapped data - The remarkable-day category - Previously generated reports to reduce repetition - The user’s country for appropriate spelling and vocabulary - Outputs were improved through prototype comparisons, LLM-based judging, human review, and feedback from creative, technical, and safety teams. ## Distilling the Model for Scale - Larger frontier models produced strong results during prototyping but were too expensive for more than a billion generations. - Spotify generated high-quality reference outputs and curated them into a reviewed “gold” dataset. - A smaller, faster production model was fine-tuned on that dataset. - Direct Preference Optimization (DPO), based on curated human A/B evaluations, further aligned the smaller model with the preferred output style. - The resulting model achieved preference performance comparable to the larger baseline. ## Generating 1.4 Billion Reports - Approximately 350 million users were eligible, with up to five reports each. - Spotify pre-generated about 1.4 billion reports before Wrapped launch. - The system sustained thousands of model requests per second over several days. - After remarkable days were computed, snapshots were published to a pub/sub queue. - Reports were generated sequentially per user so earlier reports could inform later ones and prevent repetition. - Real-time dashboards tracked throughput, reliability, errors, and projected completion time. - The generation engine ran continuously for four days, followed by checks for missing reports, inconsistencies, and necessary re-generation. ## Designing Storage for Concurrent Writes - Completed reports were stored in a distributed, column-oriented key-value database optimized for high-throughput writes. - Each user occupied a single row, with separate columns representing completed remarkable days. - Instead of maintaining a serialized list—which could cause race conditions during read-modify-write operations—each date received its own column qualifier in `YYYYMMDD` format. - Independent reports could therefore be written concurrently to separate cells without locks or coordination. - Report content was written first, followed by lightweight metadata marking the report complete. - This ordering prevented the system from exposing a completion marker before the underlying report was safely stored. ## Practical Conclusion Building Wrapped Archive required treating creative AI generation as a large-scale production system: ground outputs in structured data, use smaller specialized models when volume demands it, evaluate continuously, and design storage schemas that make concurrency safe by default.
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