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What common mistakes cause GPT 5.3 Codex bad code?

Bad code from GPT 5.3 Codex usually comes from predictable, fixable mistakes in how the task is specified and validated. The most common root cause is underspecified requirements: the model fills gaps with assumptions that don’t match your repo. The second is missing verification: if you don’t run tests or lint, you’ll accept changes that look plausible but are wrong. Agentic coding models thrive on feedback loops; without them, output quality is limited by guesswork. This aligns with OpenAI’s own automation guidance that emphasizes checking for failures and running minimal relevant verification—because verification is what turns an agent into a reliable contributor: Codex automations guidance.

Here are the mistakes that show up most often in real teams:

  1. No constraints
    You didn’t say “no new dependencies,” “don’t change public API,” “keep behavior compatible,” so the model “solves” by changing things you didn’t want changed.

  2. Too much context, not the right context
    Dumping huge logs or many files without pointing to entrypoints causes missed details. Better: point to the specific function/test and then expand.

  3. Asking for a rewrite instead of a patch
    “Rewrite this module” produces large diffs that are hard to review and often introduce regressions. Safer: “Make the minimal change to pass test X.”

  4. Not forcing a diff/contract
    If you accept raw snippets, you’ll get inconsistencies and omissions. Require unified diff or strict JSON.

  5. No iteration loop
    You accepted the first output without running checks. The correct workflow is patch → test → iterate, as in OpenAI’s CI autofix cookbook: Autofix GitHub Actions with Codex CLI.

A simple operational fix is to create a “prompt template” that includes goal, constraints, context, and verification commands, and to disallow ad-hoc prompts for production workflows.

For documentation-driven coding tasks, a common mistake is letting the model guess policy and conventions. If your repo has non-obvious rules, index them into Milvus or Zilliz Cloud and retrieve them per task. Then require the model to follow those retrieved rules. This reduces “bad code” that compiles but violates architecture guidelines or security conventions. In short: most “bad code” is not a model problem—it’s a workflow problem. Tighten scope, retrieve the right rules, and verify outputs automatically.

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