Scale AI Feature Trails

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Multilingual AI Safety Benchmarking

ROK-FORTRESS is a new benchmark developed to evaluate AI safety across languages and geopolitical contexts, moving beyond translation-only evaluations. It systematically adapts linguistic form and contextual grounding to measure model behavior, revealing that language and context interact in complex ways and that translation-only benchmarks can misestimate real-world safety gaps. This contributes to the development of more realistic AI safety evaluations that reflect diverse deployment conditions.

1 post · 2026

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Agent Failure Diagnosis

Insights Generator is a system designed to help engineers diagnose and fix failures in AI agents by analyzing their full run history. It identifies failure patterns, quantifies their occurrence, and provides specific trace evidence, significantly improving engineer productivity in debugging and enhancing agent performance. This post details the system's rollout to Scale GenAI Platform customers as a diagnostic layer for production agents, enabling continuous improvement loops by surfacing an inc This post extends the Agent Failure Diagnosis capability by introducing a framework for determining when human intervention is economically justified in an agentic workflow. It defines high-value human work by comparing the expected loss from an agent's error against the cost of human review, incorporating factors like credit value, agent confidence, and the asymmetry of potential losses (e.g., forgone credits vs. escalated invalid credits). This allows for a risk-adjusted threshold for human review, optimizing the allocation of limited human capacity to decisions with the greatest expected value.

3 posts · 2026

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Data Labeling Systems

This post details how Scale AI is building national AI infrastructure for Qatar, emphasizing a shared architecture where components developed for one use case can be inherited by others. It highlights the application of AI in judicial, education, employment, and cultural sectors, underpinned by common capabilities like oversight, evaluation frameworks, data operations, and an upskilling layer. The focus is on augmenting human expertise and ensuring AI systems are sustainable through human-centered design. This guide provides a comprehensive reference for data labeling and shares practical best practices derived from Scale's extensive experience in addressing the most significant problems in data labeling, covering supervised learning, data types (images, video, 3D, audio, text), and annotation strategies (automated, human-only, human-in-the-loop).

5 posts · 2026

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Diffusion Models

This post introduces diffusion models as a powerful generative capability for image creation. It explains their underlying principles, compares them to other generative models like GANs and VAEs, and highlights their advantages in image realism and stability. The guide covers practical aspects such as getting started with web applications and local installations, and delves into prompt engineering techniques for controlling image generation. It also touches upon the limitations and future applications.

1 post · 2026

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Machine Learning Model Training

This post expands on the foundational guide to machine learning model training by focusing specifically on Large Language Models (LLMs). It details what LLMs are, their historical development, importance, and common use cases such as classification, text generation, summarization, question answering, search, and software programming. It also introduces key concepts for working with LLMs including model size and performance, fine-tuning, Reinforcement Learning from Human Feedback (RLHF), and prompt engineering. The post emphasizes the need for businesses to fine-tune LLMs on proprietary data to unlock their full potential and provides industry-specific examples. It aims to help organizations understand and adopt LLMs for their use cases.

2 posts · 2026

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