LinkedIn consolidated five separate retrieval systems into a single fine-tuned LLaMA-3 language-model dual encoder that serves the entire feed through nearest-neighbor search at sub-50-millisecond latency, and its key insight is that feature representation—not the model—is where the real effort lies.

LinkedIn replaced five independently optimized retrieval systems with a single dual encoder built on a fine-tuned LLaMA-3, converting members and posts into one shared embedding space via a prompt library that turns structured features into text. The general lesson: feeding raw popularity counts as integers had almost no correlation with relevance scores, while converting counts into ranked percentages raised correlation sharply and improved retrieval accuracy by roughly fifteen percent. ✦ AI generated

Author (article How to Fight Clickbait: Meta, LinkedIn & YouTube Case Studies) · ByteByteGo Newsletter · 2026-08-10 · original ↗

In March 2026, LinkedIn replaced those systems with a single retrieval model built on a fine-tuned version of Meta's LLaMA-3. The model acts as a dual encoder, converting both members and posts into one shared embedding space. It serves the entire feed through nearest-neighbor search at sub-50-millisecond latency... A language model processes text, while a recommendation system runs on structured features such as view counts, engagement rates, work history, and post metadata. LinkedIn bridged this with a prompt library that converts structured fields into templated text sequences the model can process... When the team fed raw popularity counts directly into the prompts, the numbers had almost no correlation with the model's relevance scores, because large integers entered the model as arbitrary tokens. Converting each count into a ranked bucket, expressed as a percentage that the model could process in context, raised the correlation sharply and improved retrieval accuracy by roughly fifteen percent. The takeaway from this is that the model is often the part that works, and the surrounding representation of the data is where the real effort goes.

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