Netflix GenRec: LLMs replacing recommendation logic

GenRec: what Netflix really did

According to reports by The Decoder , Netflix compared its traditional recommendation engine with an alternative system called GenRec . This is an internally developed language model. Its task is simple in form: convert users' viewing history into structured text and use it to generate recommendations.

Netflix's traditional engine is the result of years of engineering. It relies on thousands of manually built features: genre, duration, viewing time, drop-off behavior, and affinity for actors or directors. Every single feature has been designed, tested, and optimized by dedicated teams. Therefore, replacing even a part of this architecture with an LLM is no simple choice.

Still, internal tests showed that GenRec performs better. Netflix hasn't dropped detailed metrics, but they called the experiment "an early but promising step." So the signal is crystal clear: language models can hang with—and sometimes beat—rule-based logic even in super specialized fields like recommendations.

Why the textual approach changes the rules of the game

The shift from numeric features to text representations isn't just technical. It's a paradigm shift. In fact, traditional recommendation systems require a complex pipeline: data collection, feature engineering, training on interaction matrices, and continuous rule updates. Every single change requires human intervention.

GenRec, on the other hand, treats viewing history like language. A user who watches three psychological thrillers in a row and then bails on a romantic comedy after ten minutes—that behavior becomes a text sequence. The model reads it in context, without any engineer having to spell out what that mix means.

Plus, language models are generalists by nature. So, they can catch behavioral nuances that hand-crafted features just can't model. For instance, the link between the time of day, the device used, and the favorite genre pops up implicitly, without having to build a custom feature.

This approach is reminiscent of what has already been observed in other domains: foundational models are progressively replacing specialized ML pipelines in sectors like retail, finance, and media. Recommendation is the natural next step.

Immediate impact on martech and personalization

For marketing managers, the relevant question is not technical. It is strategic: what changes in the personalization systems we already use or are considering?

First of all, reliance on manual feature engineering is bound to shrink. Marketing automation platforms, advanced CRMs, and e-commerce recommendation engines are built on logic similar to what GenRec is replacing. Consequently, vendors of these tools will need to update their architectures—or they'll lose ground to native AI solutions.

Second, the quality of personalization can improve without needing dedicated data science teams. This is especially relevant for Italian SMEs, which often lack the resources to build proprietary ML pipelines. In fact, an LLM-based approach lowers the technical barrier and makes more sophisticated personalization accessible.

Finally, the way success is measured changes. Traditional setups focus on clear-cut metrics: click-through rate, watch time, conversion. Language models bring in a more contextual vibe. So, the evaluation metrics will need to evolve right along with them.

In SHM Studio we are already seeing this transition in projects by Digital marketing that integrate advanced personalization components. The pressure towards LLM-native architectures is real and growing.

The work in progress: limits and unknowns of GenRec

Netflix was upfront about calling GenRec an experiment. There are solid reasons for this caution. First of all, language models have way higher computing costs than traditional systems. Generating real-time recommendations for hundreds of millions of users takes massive infrastructure.

Furthermore, interpretative transparency remains an open issue. Rule-based systems are explainable: you can trace why content was recommended. An LLM, by contrast, operates opaquely. This creates potential compliance issues, especially in regulated contexts like the European market, where GDPR imposes strict standards on automated profiling.

Similarly, how steady recommendations are over time is less predictable. A rule-based system acts in a predictable way. A language model can churn out different outputs for the exact same inputs, depending on the inference context. So, testing and keeping an eye on things constantly become even more crucial.

MIT Technology Review has already raised these issues in the broader context of enterprise adoption of LLMs. The point is not whether language models are superior, but under what conditions and with what guardrails.

What to do now: guidelines for marketing managers

The Netflix experiment does not require immediate action, but suggests some strategic directions to consider already in the second half of 2026.

• Evaluate the architecture of your recommendation systems. If you use an e-commerce platform or CRM with personalization engines, it is worth asking the vendor what their roadmap is toward LLM-native architectures. Platforms that do not update risk becoming obsolete.
• Invest in the quality of behavioral data. Language models feed on rich, contextual interaction sequences. Therefore, the quality and granularity of the data collected becomes a direct competitive advantage. A SEO Strategy and well-structured content helps enrich these signals.
• Explore AI solutions for content personalization. Even without Netflix-scale infrastructures, there are accessible tools that apply similar logic to GenRec. Our activities of Copywriting and google ads campaigns already integrate AI-driven personalization components.
• Monitor metrics with a contextual approach. In addition to CTR, it is useful to start measuring the consistency of personalization over time and user satisfaction along the funnel. The LinkedIn campaigns they provide a good testing ground to try out more sophisticated targeting logic.

Outlook: where this trajectory leads in 2027-2028

The GenRec experiment is an early indicator of a broader transition. In the 2027-2028 biennium, it's reasonable to expect that major martech vendors will integrate LLM components into their recommendation and segmentation platforms, and personalization systems are fully aligned with this direction.

Furthermore, the competition between hybrid approaches — which combine explicit rules and LLM inference — and purely generative approaches will be one of the core themes in the industry. It's not guaranteed that the "all-LLM" model will win out in every scenario. However, the shift toward more flexible architectures that rely less on manual engineering seems irreversible.

For Italian companies, especially those in retail and digital B2B, this means that platform decisions made today will have a significant impact on mid-term competitive ability. Relying on partners with up-to-date skills in artificial intelligence applied to marketing is no longer an optional add-on. It is a structural choice.

We at SHM Studio we follow this evolution continuously, integrating new AI architectures into the strategies of web development , SEO and Digital marketing of our clients. To learn more about how these technologies can be applied to your context, you can contact the team or explore other content on Blog .