Semantic Search Stack

LinkedIn has developed a semantic search experience leveraging LLMs to understand natural language queries and infer user goals. This system uses embedding-based retrieval (EBR) on GPUs for candidate generation and a Cross-Encoder Small Language Model (SLM) for ranking. Optimization techniques like score caching and ranking-depth control are employed for efficiency. Relevance is measured using LLM judges aligned with product policies. The EBR model is a dual-tower bi-encoder trained with contrastive loss. This post details the engineering effort behind LinkedIn Premium All-in-One, a unified product for small businesses. It focuses on leveraging AI and LLMs to create unified workflows for sales, marketing, and hiring. Key technical aspects include an LLM-based approach for inferring Target Audiences (TA) using profile and company information, with privacy guardrails. The TA definition is further refined through activity-based signals and implicit signals (buyer-side and seller-side). The system in this post details the engineering effort behind LinkedIn's data context layer, a system that combines automatically generated dataset profiles with team-maintained knowledge about meaning, usage, and limitations to help people and AI agents discover the right assets and understand their context. The system uses an AI enrichment pipeline to generate baseline context from schemas, source code, and query logs, and teams supplement this with human-curated knowledge. The generated layer uses a four-stage pipeline (nomination, collection, generation, persistence) to process signals like query logs, source code, and sample values to create structured dataset profiles and embeddings. The human-curated layer uses a Git repository and pull requests for authoring and review, organizing context into global, team, and analysis scopes.