Netflix presents GenRec, an LLM-backed recommendation ranker that post-trains an internal foundation LLM on Netflix-specific data. The system verbalizes user histories, item metadata, and context as natural language, replacing traditional hand-crafted feature engineering with 'context engineering.' A catalog-aware scoring head ranks Netflix titles, while reward-weighted training aligns recommendations with long-term member satisfaction and business goals. Served in prefill-only mode on Netflix's vLLM infrastructure for cost efficiency, GenRec outperforms a mature production ranker in A/B tests covering ~10% of Netflix traffic using 10–40x fewer labeled training examples. Ablations show Phase-1 Netflix-adapted pretraining improves ranking by 10–20% over off-the-shelf LLMs, and Phase-2 post-training adds another 35–80% gain. Context compaction reduces token usage to one-third with negligible quality loss. The work signals a broader shift from custom RecSys architectures toward shared LLM foundation backbones with clearer scaling laws.
