Meta-Prompting Quiz
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.
Meta-Prompting Quiz
Test your knowledge of automated prompt optimization, DSPy, APO, evolutionary prompting, and LLM-as-judge 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. A strong model rewrites your prompt and it reads much better. What do you do before shipping?
2. Why must the optimiser never see the hold-out set?
3. Candidates scored on a large model look great, but production uses a small one. What is the risk?
4. The tuning score keeps rising, but the hold-out score has been flat for two rounds. What now?
Continue reading
All Prompt Engineering articles →Red Teaming LLM Applications: Attack It Before Your Users Do
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.
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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