A good experiment starts with a clear hypothesis. Before you even open an AI interface, you need to know exactly what you are trying to prove or disprove. This requires moving beyond vague ideas like "AI will make this faster" to specific, measurable claims. By framing your hypothesis carefully, you set the stage for an experiment that yields actionable insights rather than just interesting anecdotes.
The CORE-Sandbox framework provides a structured environment for this testing. By systematically evaluating Capabilities, Opportunities, Risks, and Ecosystem connections, you can understand not just if an AI tool works, but how it fits into your broader workflow. Remember to test one variable at a time and document everythingâdeliberate friction in the setup phase prevents wasted effort during execution.
Assignment
Identify a specific task in your workflow. Draft a hypothesis using the provided format, and outline a simple experiment to test it using the CORE-Sandbox framework. Document your expected outcomes.
Learning Objectives
- Formulate a clear, testable hypothesis for an AI experiment.
- Apply the CORE-Sandbox framework to evaluate AI capabilities and risks.
- Design an experiment that isolates a single variable to measure improvement.
The Hypothesis Format
A good experiment starts with a clear hypothesis. Use the format: 'For [specific step], using [specific model] with [specific prompt approach] will [measurable improvement] compared to [current method].'
CORE-Sandbox Framework
A structured approach to testing AI: Capabilities (what can it do?), Opportunities (where does it create value?), Risks (what happens when it fails?), and Ecosystem (how does it connect?).