Training
Red Teaming
Red teaming means deliberately attacking a model or system to find harmful outputs and failure modes before real users or attackers do.
In depth
Red teaming means deliberately attacking a model or system to find harmful outputs and failure modes before real users or attackers do. Anthropic's 2022 study red teamed models from 2.7B to 52B parameters and found that RLHF-trained models got harder to red team as they scaled. It also released a dataset of 38,961 red team attacks. Labs now report red team findings when they launch models, and teams shipping agents run the same exercise against their own tools, permissions, and prompts.
Primary source: arxiv.org
Example
In practice, developers reach for Red Teaming when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Red Teaming?
Red teaming means deliberately attacking a model or system to find harmful outputs and failure modes before real users or attackers do.
Why does Red Teaming matter for AI developers?
Red Teaming sits in the Training part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Red Teaming?
Developers Digest publishes tutorials and videos that cover Training topics including Red Teaming. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
RLHF (Reinforcement Learning from Human Feedback)
A training technique that fine-tunes a model using human preference judgments.
Training
RLAIF
Reinforcement Learning from AI Feedback: a variant of RLHF where an off-the-shelf LLM, not human annotators, labels which of two responses is better.
Training
Transfer Learning
The technique of taking a model trained on one task and adapting it for a different but related task.
Put this concept to work
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