# scribd Published articles for scribd. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A Repeatable Human-in-the-Loop Process for Large-Scale LLM Classification DevFeed: [A Repeatable Human-in-the-Loop Process for Large-Scale LLM Classification]() Original publisher: [Read original article]() Author: Anish Kumar Published: 2026-07-11T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Large Language Model](), [Prompt Engineering](), [Inference](), [datasets](), [data]() Tags: [asynchronous](), [batch](), [content-trust-series](), [cost](), [data](), [databricks](), [datasets](), [evaluation](), [featured](), [inference](), [llm](), [machinelearning](), [models](), [production](), [prompt](), [scribd](), [workflow]() ### AI overview The article presents a repeatable human-in-the-loop process for large-scale LLM classification. It combines fast-model labeling, judge-model disagreement detection, targeted SME review, a golden dataset built from corrections, and selective prompt iteration. ### Source excerpt LLM models change. Prompt quality changes. Cost changes. We assumed that from day one. ## Dual-Embedding Trust Scoring DevFeed: [Dual-Embedding Trust Scoring]() Original publisher: [Read original article]() Author: Eric Chang Published: 2026-02-25T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [trust and safety](), [Embeddings](), [Artificial Intelligence](), [Machine learning](), [data](), [datasets](), [genai]() Tags: [ai](), [content-trust-series](), [data](), [dataset](), [embeddings](), [featured](), [generative-ai](), [machine-learning](), [machinelearning](), [pii](), [privacy](), [research](), [scribd](), [trust](), [trust-and-safety]() ### AI overview Scribd describes a Content Trust Score that combines Generative AI signals, proprietary multilingual embeddings, and classical machine learning to assess the severity of documents violating defined trust and safety pillars. The research covers illegal, explicit, privacy/PII, and low-quality content, using annotated data from roughly 100,000 documents. ### Source excerpt Scribd is a digital library serving academics and lifelong learners, offering hundreds of millions of documents. This very nature presents a significant concern: content trust and safety. Protecting our library from undesirable and unsafe content is a top priority, but the multilingual and multimodal (text and images) nature of our platform makes this mission very challenging. Also, while third-party tools exist, they often fall short, lacking the nuance to handle our specific trust and safety categories. ## Engineering Around Extreme S3 Scale at Scribd DevFeed: [Engineering Around Extreme S3 Scale at Scribd]() Original publisher: [Read original article]() Author: R Tyler Croy Published: 2026-02-10T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Amazon S3](), [Cloud](), [data](), [Amazon Web Services]() Tags: [aws](), [aws-s3](), [cloud](), [featured](), [s3](), [scale](), [scribd](), [talk](), [youtube]() ### AI overview Scribd discusses engineering challenges at extreme AWS S3 scale, including checking whether files are damaged, controlling costs, and consolidating objects to reduce spending. The discussion is also available as audio and video. ### Source excerpt Scribd has absolutely fascinating data-at-scale type problems, all the way down to the fundamentals of how we use AWS S3. In my previous post I wrote about the design of Content Crush and how Scribd is consolidating objects in S3 to minimize our costs. Related to that work I was fortunate enough to join the (in)famous Corey Quinn to talk about Engineering around Extreme S3 scale: