Use AI to Boost Developer Productivity (www.docker.com)

A senior engineer shares a practical workflow for actually getting productivity gains from coding AIs—warning that the “5x” social-media claims aren’t automatic and that success requires disciplined software-engineering habits. The core message: treat AI as a tool, not a magic wand. The author recommends an iterative four‑phase cycle—Prompting, Planning, Producing, Refining—used with agentic coding tools like Claude Code. That disciplined loop helps avoid context drift, hallucinations, and low-quality outputs that arise when an agent accumulates messy conversation state or is given large, ambiguous tasks. The summary includes concrete tactics: aggressively manage context (wipe conversation state between tasks or store persistent facts in markdown files like CLAUDE.md), decompose work into small, testable prompts, chain prompts (use a general LLM to draft detailed prompts for a coding agent), and capture reusable prompts as custom commands (example: auto‑generate Postman collections from controllers/tests). In Planning mode, review and iteratively approve the agent’s proposed changes to catch overbroad rewrites; during Producing, guide the agent in real time and audit its output for naming, constant usage, and conventions. For the AI/ML community, this shows that model utility depends on interface features (planning mode, levers), prompt-engineering patterns, and team processes—practical levers for turning probabilistic models into reliable developer assistants.