Most developers use Claude Code the same way after six months that they did on day one: ad hoc, reactive, one request at a time. The practitioners who've gone deeper have built actual systems around it, and the efficiency gap shows up in costs, speed, and focus.
Three areas produce the biggest gains.
Git automation is usually the first optimization people find. Claude Code can handle commit messages, branch names, and pull request descriptions automatically while you stay in a coding flow. The time saving is real but the focus benefit is larger - writing a coherent commit message mid-problem forces a context switch that costs more than the two minutes it takes.
Context management is where real cost reduction happens. Claude Code sends your project's code as context with each request, and that context drives the token count - tokens being the unit AI models use to measure and charge for the text they process. A 200,000-token session costs roughly four times what a 50,000-token session costs, with no guarantee the extra context improves the output. Practitioners are using .claudeignore files (similar to .gitignore) to exclude directories the AI doesn't need to see, segmenting large projects into focused sessions, and structuring work so each conversation has a clear scope. Better scoping often produces better results alongside lower costs.
Custom commands - slash commands you define yourself - let you codify any workflow you repeat. Instead of explaining your preferred test structure or commit message format on every session, you write the instruction once and invoke it with a short command. Over weeks of use, these compound into meaningful time savings.
The deeper pattern is that Claude Code rewards treating AI assistance as a workflow design problem. The developers getting the most from it aren't necessarily writing better prompts - they're reducing the noise around their prompts and building habits that preserve coding focus.