Generate → Evaluate → Refine
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-prompt asks the model to engineer a better prompt
Asking an experienced chef to write the recipe before you cook.
Instead of writing the prompt yourself, you ask the model to write it for you.
> Meta-prompt sent > Task: summarize tech docs > Requirements: junior devs, simple, examples
Meta-prompt asks the model to engineer a better prompt
Asking an experienced chef to write the recipe before you cook.
Instead of writing the prompt yourself, you ask the model to write it for you.
> Meta-prompt sent > Task: summarize tech docs > Requirements: junior devs, simple, examples
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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