Musubi Resources: Insights & Best Practices

Agreement Observability for AI Moderation

When AI moderation drifts in production, the signal often comes too late. This post walks through the Agreement Observability tool we built to track model-moderator agreement in real time and simulate threshold tradeoffs before they become production problems.
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How Feeld used AiMod to block scams faster

Dating app case study: How Feeld's moderation team makes 500x faster decisions with 89% fewer mistakes. Covers bot detection, automation, and human-in-the-loop systems.

The Top Challenges of Using LLMs for Content Moderation (and How to Overcome Them)

Technical guide to LLM content moderation infrastructure: model selection, integration architecture, accuracy measurement, bias mitigation, team structure, and human oversight. Covers common deployment pitfalls and tested solutions from teams running production systems at scale.

How to use LLMs for Content Moderation

A practical guide to LLM content moderation from expert T&S practitioners. Learn prompt / policy engineering, model selection, and real-world strategies for 90%+ accuracy.

The Future of T&S is Better Collaboration

Most companies treat T&S like a cost center staffed by disposable contractors. Here's how to position T&S as strategic partners: building exec relationships, demonstrating ROI, and creating collaborative workflows with product/legal/ops. Includes implementation playbook.

Can LLMs moderate nuanced policies?

With strong prompts and diverse training data, LLMs can distinguish harassment from banter, sexual content from sex ed, and satire from hate speech. Requires context examples, edge case coverage, policy engineering, and human calibration. Practical guide with real examples.