#Prompt Engineering
4 total
Prompt Engineering for Reasoning Models: When to Use Them, How to Prompt, How to Save Tokens
Reasoning models need different prompts than regular GPT models: the official best practices -- simple and direct beats technique stacking, no step-by-step coaching, seven task types worth delegating, and reasoning-item token savings.
Read articlePrompt evaluation and failure debugging: LLM-as-judge + regression tests + Debug mode
After prompts go to production: LLM-as-judge auto-scoring, regression test sets, failure case taxonomy (hallucination / off-topic / format error), Debug mode logging, token / cost visualization.
Read articleGPT-5.6 coding prompt patterns: 12 templates for Codex / Cursor that get it right on the first try
12 GPT-5.6 family prompt templates optimized for coding: architecture understanding, incremental implementation, bug localization, code review, test generation, refactoring, dependency upgrades. Pair with Codex CLI / Cursor / Aider.
Read articlePrompt Engineering Core Patterns: 8 Templates That 2× GPT Output
A systematic walkthrough of 8 high-frequency prompt patterns — role prompting, few-shot, chain-of-thought, ReAct, self-consistency — each with a reusable template.
Read article