How Examples Shape Model Output
Selecting, ordering, and structuring input-output examples to reliably steer model behaviour — the most effective prompting technique for consistent formatted output.
Animate: How Examples Shape Model Output
Step through the flow one stage at a time and watch the data move.
Reading Few-Shot Prompting is free. Pro unlocks the other four modes on all Prompt Engineering topics, so you can go from understanding it to building it.
- SeeThe architecture as diagrams, so you can hold the whole system in your head.
- AnimateStep through the flow one stage at a time and watch the data move.
- TestScenario questions with explanations that show exactly where your model of it breaks.
- BuildA guided implementation with code you can run and adapt for production.
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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.
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.
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