Tag: Practice

Where LLMs sharpen product discovery and where they quietly hollow it out — a stage-by-stage guide to the dos and don'ts.

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.

How to wire a library of example files into Claude Projects, Cowork, and Claude Code so the right exemplars are selected per task and your quality bar compounds instead of resetting every session.

Using a model to build prompts as artifacts, either developing a reusable prompt on purpose or extracting one from a chat that already worked.

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.

Six named structures for prompts — CO-STAR, RISEN, RACE, CREATE, APE, and STOKE — with the components each one forces you to specify and a decision matrix for picking one.

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.