AI

AI is only leverage if it lands inside a practice. These techniques cover where AI accelerates the work — discovery, delivery, decision-making — and where it just generates more for you to review.

Compounding Engineering

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

Evaluations

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

Few-Shot Example Integration

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.

Meta-prompt Engineering

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

Prompt Frameworks

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.

Reverse Prompt Engineering

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