A cobalt Becoming an AI Power User hardcover beside context, iteration, evaluation, and saved-work cards.

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Becoming an AI Power User

How experienced AI users get more from the same tools. Learn to improve a weak response, test a model on your own work, save what you discover, and hand off more of a workflow when the results earn it.

38 chapters In development

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Introduction1 chapter

What power users do differently with AI

1The Expanding Edge11 chapters

1.1They widen what they can do with AI by practicing one step past what already works
1.2They build the setup of prompts, examples, and checks that produces their AI work
1.3

2Working With the Model5 chapters

2.1They brief AI like a collaborator because search-engine defaults quietly hold the work back
2.2They match the input medium to the task because voice, text, and images each capture different context
2.3They follow up until the output matches because the first response is a measurement
2.4They use AI to think through unsolved problems instead of asking it to produce finished answers
2.5They choose the collaboration mode before the task because directing, fusing, and delegating produce different outcomes

3Decomposition and Parallel Work3 chapters

3.1They separate complex tasks into data, reasoning, and presentation so each part can be checked
3.2They give conversations clear responsibilities and check how results fit together
3.3They test a separate reviewer before trusting it on important work

4Calibration and Evaluation5 chapters

4.1They choose models by measured results
4.2They regularly test their AI tools at the edge of their capacity to learn where reliability ends
4.3They verify AI claims against sources because confident wording does not establish accuracy
4.4They build evaluation workflows with rubrics, examples, and judges to scale quality standards
4.5They maintain calibration as models change because every AI opinion has an expiration date

5Infrastructure and Learning Systems7 chapters

5.1They save reusable AI assets so later sessions can use what they learned
5.2They package instructions, references, and standards into workspaces that prepare recurring tasks
5.3They keep prompts, specs, and workflows in formats they own, so many capable models can run them
5.4They keep one home for their standards so tested improvements can reach every project
5.5They go beyond chat to agents that read, write, search, build, and take action
5.6They build learning loops that test and carry improvements into future work
5.7They match delegation to the task and the checks available

6Workflow Redesign, Creativity, and Growth6 chapters

6.1They ask AI where AI could help next instead of waiting for use cases to occur to them
6.2They redesign entire workflow sequences around AI instead of just speeding up individual steps
6.3They create personalized software, thoughtful gifts, and solutions tailored to exactly how they live and work
6.4They turn a private AI workflow into a shared playbook, so a whole team keeps improving and evolving it instead of one person
6.5They teach what they learn and seek out other power users to test and expand how they work
6.6They practice regularly and carry tested improvements into later work