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.