Red Teaming LLMs Quiz
Threat model first, then manual sessions, then automated scans with garak, promptfoo and PyRIT. How to find jailbreaks, data leaks and unsafe tool calls, fix them in the architecture, and keep them fixed in CI.
Red Teaming LLMs Quiz
Test your knowledge of LLM adversarial testing, Garak, PyRIT, automated red teaming, and responsible AI evaluation.
Apply the decisions to the scenario
Each question tests one decision from Read, not a definition. The explanation names the decision it checks.
1. Where should a bank assistant’s red team spend most effort?
2. An attack failed once in testing. Is the feature safe from it?
3. You fixed a prompt-extraction issue in March. In June it is back. What was missing?
4. Why not rely only on automated attack generation?
Continue reading
All Prompt Engineering articles →Meta-Prompting: Letting a Model Tune Your Prompts
From a one-off rewrite to refinement loops on failures, candidate search and compiled DSPy programs. How to optimise prompts against a metric without overfitting, and how to review what the optimiser produces.
Tree-of-Thought: Search Over Reasoning Paths
Propose several next steps, judge the partial states, keep the best and expand them. How to build a Tree-of-Thought search, why the evaluator decides everything, and when a reasoning model makes it unnecessary.
Constrained Generation: Beyond Hope-Based Parsing
Enforce output structure at the token level with grammars and token masks — how it differs from native JSON mode, and when the strictness costs you reasoning quality.
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