How I Prompt AI Models in 2026 vs a Year Ago: 3 Key Changes

A regular user of Claude and Codex shares three concrete changes that transformed how they prompt AI models over the past year. The advice is practical, no-nonsense, and aimed at developers using AI coding agents regularly.

1. Switch from Prompt Templates to Skills

Prompt templates are static and hard to share. Instead, the author now uses skills — formalized prompt templates that live inside Claude, Codex, or on GitHub. A skill is written once, reused across projects, and easily shared with teammates. This replaces the old approach of everyone maintaining their own prompt doc.

2. Prioritize Goals Over Step-by-Step Instructions

The second change: stop writing rigid, step-by-step instructions. Instead, write the skill or spec like you would for a senior developer: clearly describe the goal, what success looks like, and provide all necessary context. Modern models handle step decomposition better on their own. As models improve, this approach remains valid without rewriting skills every time a new model drops.

3. Use Loops Instead of Interactive Prompting

For complex, long-running projects (e.g., building software, websites, or long analyses), write a solid spec document and have the model run a loop against it. In Claude Code, the /loop command lets the model self-prompt continuously against the spec for hours. Codex has the same feature. This replaces the manual prompt-review-repeat cycle.

The author notes that skills plus goal-over-steps made the biggest difference and recommends starting there if you only change one thing.