{
  "id": 17173653,
  "title": "Do you log your LLM prompts and responses? Four answers, and the one I'd pick",
  "url": "https://urgent.news/2026/10/30/do-you-log-your-llm-prompts-and-responses-four-answers-and-the-one-id",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T04:18:28.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/m_t_ramkrushna/do-you-log-your-llm-prompts-and-responses-four-answers-and-the-one-id-pick-4hmd"
  },
  "original_language": "en",
  "account": "The wire material presents four options for logging prompts and responses of language models (LLMs), along with a personal preference for option C. The key points are:\n\nOption A (no logging) means no sensitive data is stored or leaked, but it also makes debugging impossible as there is no evidence of what was input and output by the LLM.\n\nOption B (metadata only) retains basic information like token counts, latency and error rates. This allows for basic cost and performance monitoring, but doesn't provide insights into why the model generated incorrect or sensitive outputs.\n\nOption C (full text with redaction and short retention) is what the author recommends. It allows reproducing real failures and sampling production traffic for evaluation, while limiting the risk of sensitive data leakage by masking personal information and keeping logs for only a short period of time. Access to the logs should be restricted.\n\nOption D (log everything forever) provides maximum debuggability, but poses the biggest risk of allowing sensitive user data to be exposed and kept indefinitely.\n\nThe author starts with option B for initial development, moves to option C once the LLM feature is in production, and avoids option D entirely due to the significant privacy and security risks involved. The policy around logging should be documented in the privacy notice, offering users an opt-out if desired.",
  "summary": "Friday AI Question. This week's question is one every team shipping an LLM feature has to answer, usually after the first weird bug report: Do you log your LLM prompts and responses? A. No. We don't store prompts or outputs at all. B. Metadata only. Model, token counts, latency, cost, finish reason, user/session IDs. No text. C. Full text, redacted, short retention. Prompts and outputs are kept,…",
  "key_points": [
    "Option A (no logging) prevents sensitive data storage but hinders debugging.",
    "Option C (full text with redaction) balances debugging needs with privacy protection."
  ],
  "editors_take": "Balancing debugging needs with data privacy requires a tiered logging strategy that prioritizes automated redaction and limited retention to mitigate the risks of exposing sensitive user information.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}