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Overview

How Promoted Works
- Promoted Integration Walkthrough - Hipcamp
Installing Promoted on Top of Other Search Systems
Getting Started

Integration

Overview
Delivery
Metrics
Content
Sending Embeddings
Debugging
- Debugging Integration Issues
- Metrics Library for iOS Performance Debugging Using OSLog and Xray

Explanation

Attribution and Joining Logic
Handling Position Bias
Non-Engagement Features
Using LLMs to Generate Relevance Labels
GPU LLM Inference
Tracking
Technical Diagrams
Promoted Q&A
Glossary
Default Semantic Relevance Rubric

Selected Capabilities

Native Ads
Organic Search and Feed
Unified Organic + Native Ads
- Ad Load, Slotting, and Filtering
- Long-Term Value Optimization

Evaluation and Improvements

A/B Testing
Estimating Impact using the Shadow Tier
Daily ML Reports
Running a Holdback Test
Introspection Report
Feature Importance Report

Security and Legal

Pseudonymization and Disabling Personalization
Privacy and Security FAQs
- GDPR Subprocessor List

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GPU LLM Inference

How Promoted uses Large Language Models (LLM) and GPU inference in live delivery.

Updated about 8 hours ago

Non-Engagement Features