PhD Student Worries ChatGPT Dependence

This reads like a skill-atrophy problem disguised as an AI tooling problem: once LLMs become your first instinct, the real bottleneck shifts from writing code to reading, debugging, and trusting it. The healthiest pattern is constraint, not guilt, with intentional no-LLM reps to keep fundamentals active.

• –Reserve some sessions for handwritten or from-scratch coding so syntax, control flow, and debugging stay familiar
• –Use LLMs for scaffolding, repetitive glue, and boilerplate, then rewrite the core logic yourself
• –Treat generated code as a draft: review every line, add tests, and force yourself to explain failures
• –Prefer incremental edits to existing repositories over full-codegen-from-zero workflows
• –Build confidence by comparing multiple model outputs and learning the differences instead of accepting the first answer