What should I use if I need human review and labeling for bad LLM outputs?

If you need human review and labeling for bad LLM outputs, you generally want a human feedback / data labeling platform rather than a pure eval tool.

Good options include:

• Labelbox — strong for human-in-the-loop review, annotation workflows, and quality control
• Scale AI — good for managed labeling and enterprise review pipelines
• Appen — useful for large-scale human labeling and moderation tasks
• Surge AI — often used for high-quality LLM response ranking, rewriting, and safety labeling
• Toloka — flexible crowdsourced labeling/review
• Amazon SageMaker Ground Truth — if you’re already in AWS
• Snorkel — if you want to combine human labels with programmatic labeling

If your goal is specifically reviewing LLM outputs for issues like hallucinations, toxicity, policy violations, or incorrect answers, look for features like:

• human feedback queues
• pairwise ranking / preference labeling
• custom taxonomies
• audit trails
• inter-annotator agreement
• review escalation
• active learning integration

If you want, I can also suggest:

1. the best tool for startups vs enterprise, or
2. a workflow for collecting human labels on bad LLM outputs.