Standardizing team AI workflow
Your team adopted AI agents organically, and now everyone prompts differently: some paste huge context and get good results, others one-line their requests and merge whatever comes back, and the codebase is drifting toward inconsistent patterns because each person's agent invents its own conventions. You're asked to onboard a new teammate and, while you're at it, establish a shared AI workflow. What concretely do you put in place so the team gets consistent, reviewable output instead of a different agent personality per engineer?
codify_conventions(dimension_names: list[str], choices: list[list[str]]) → list[str][["error-handling","naming","test-style"],[["wrap","wrap","wrap"],["camel","snake","camel"],["table","table"]]]out["naming=camel"][[],[]]out[][["layering"],[["a","b"]]]out["layering=a"]Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.