I'm a junior front-end developer and have mostly used Claude by providing Figma screenshots and some context, letting it implement the feature, then doing a quick review before opening a pull request. I haven't paid much attention to how the tool works or how much context and token usage my prompts consume. My access is about to become more limited, but I have 10 days of unlimited usage to improve my workflow. What should I study or practice to understand Claude Code more deeply, reduce unnecessary token usage, and prepare reusable skills, project instructions, or other helpful resources? I also keep hearing about vector databases and skills, but I'm not sure when they're useful or how to build them. I'd like to become much more effective while still understanding and reviewing the code myself.
2 Answers
Spend the 10 days building a repeatable workflow. Have Claude make a small change, then immediately ask it to explain the files it touched, list possible failure cases, and suggest how to test the change. For front-end work, practice Playwright, reliable selectors, network mocking, browser debugging, accessibility checks, and testing loading and error states. The key skill is recognizing when generated code is brittle or incorrect, not getting Claude to produce the largest possible patch.
To reduce token usage, keep requests focused and provide only the relevant files or excerpts instead of repeatedly sending the entire project. Add a short project instruction file describing conventions, scripts, architecture, and testing commands, and maintain concise reusable prompts for common tasks. Ask for a plan before a large edit, approve the plan, and then have the work done in smaller steps. Vector search is usually unnecessary for a small front-end project; it becomes useful when you need to retrieve relevant information from a large documentation or codebase. Start with clear project context, good tests, and a clean repository before reaching for advanced tools.
You can also ask for a test plan before implementation, then compare the final code against that plan. This makes it easier to catch missing cases and keeps the model from quietly changing the scope.