Tag: Practice

Building reusable AI setups (projects, custom instructions, examples) that improve with use, turning one-time prompt wins into durable productivity systems.

The practice of supplying AI systems with the right background information, in the right format, to produce useful outputs.

Systematic methods for measuring AI output quality so you can tell whether your prompts, context, and setups actually work.

Using AI to help you write, critique, and improve your own prompts, creating a feedback loop for prompt quality.

Thinking deliberately about how you work, surfacing habitual patterns and tacit knowledge so you can identify opportunities for improvement or automation.

The practice of structuring instructions to AI systems for reliable, high-quality outputs through deliberate choices about structure, specificity, and iteration.

A decision-rights framework that clarifies who recommends, agrees, performs, provides input, and decides.

Using AI to analyze successful outputs and reverse-engineer the prompts that would produce them, accelerating prompt development.

A strategic mapping technique that visualizes the value chain and plots components by their evolutionary stage.