A coding book provides sequence, depth, and carefully selected practice. AI adds responsive feedback. The strongest workflow uses both while requiring you to predict, write, test, and explain the code yourself.
Preview before asking
Read the objective and predict what the example will do. Ask AI to question your prediction instead of immediately supplying an explanation.
Change every example
After an example works, modify the data, interface, rules, or error handling. Ask AI for constraints or test cases rather than a completed replacement.
Use AI during debugging
Share the exact error and your current theory. Ask for the smallest diagnostic step. This preserves the reasoning that turns a fix into reusable understanding.
Finish with retrieval
Close the example and rebuild its central idea from memory. Then explain the data flow in plain language and compare your version with the book.