Explore code prompts for AI tools
Turns a messy module into a staged refactor plan that preserves behavior. It identifies seams, proposes an incremental sequence of changes with checkpoints, defines the tests and characterization coverage needed to prevent regressions, and includes rollback/feature-flag guidance for safe delivery. Best for legacy code, large components, and “we need to clean this up without breaking prod”.
Reviews a code change like a senior engineer before merge. It finds correctness issues, edge cases, API contract problems, performance pitfalls, and security/privacy risks, then outputs an ordered fix plan with concrete code edits (pseudo diff), a targeted test plan, and a safe rollout and monitoring checklist. The “assumptions and proceed” rule keeps it moving even if context is incomplete, while staying specific and implementation ready.
It guides the model to triage and resolve a bug with a structured, evidence-driven workflow. It produces exactly five distinct investigation and mitigation approaches, each with a clear hypothesis, the precise system areas to inspect, the specific logs/metrics/traces or SQL to collect, and an ordered checklist to confirm or falsify the hypothesis. It then ranks the approaches by fastest path to high-confidence resolution (including blast-radius and data-integrity risk) and performs a second-pass cross-comparison with scenario playbooks (e.g., PROD outage, data corruption risk, flaky issues) to refine the recommended path and output the first 10 concrete actions. By forcing explicit assumptions when context is incomplete, it reduces guesswork and lowers hallucination risk while keeping the plan executable.
It forces the model to produce an implementation-ready plan for a feature by generating exactly five distinct approaches, each with concrete architecture, code structure, and database design (including keys and indexes). It then ranks the approaches with explicit trade-offs and risks and runs a second-pass cross-comparison to refine the final recommendation under different scenarios. The “state assumptions and proceed” rule reduces vague back-and-forth and makes uncertainty explicit, which lowers hallucination risk and pushes the output toward actionable, verifiable engineering decisions.