{
  "id": 3355292,
  "title": "How to evaluate LLMs before production",
  "url": "https://urgent.news/2026/08/25/how-to-evaluate-llms-before-production",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-25T21:35:11.000Z",
  "source": {
    "name": "GitHub Blog",
    "slug": "github-blog",
    "url": "https://github.blog/ai-and-ml/llms/how-to-evaluate-llms-before-production/"
  },
  "original_language": "en",
  "account": "The evaluation process of an LLM system before deployment is crucial to ensure it performs as expected in real-world scenarios. Benchmarks and curated datasets are helpful for prototyping, but they may not accurately reflect production conditions. Real-world inputs can be ambiguous, labels inconsistent, and context may be missing or truncated. Moreover, edge cases that rarely appear in benchmarks can become common sources of failure. Even if offline metrics improve, they may not accurately represent production behavior.\n\nThe key is to define the product decision first, rather than focusing on the model itself. For instance, in a secret-scanning system designed to reduce false positives, the primary objective is to improve precision, while recall serves as a safety constraint. The evaluation criteria are organized into three levels: primary outcome, security constraint, and operational guardrails. This helps prevent treating all metrics as interchangeable and ensures that changes do not violate predefined thresholds.\n\nTreat offline evaluation like integration testing, repeating it anytime a significant change is made to the prompt, model, input construction, or system logic. Record the prompt, model, dataset version, and system configuration to enable comparison with baseline results. Change one major variable at a time to determine the cause of a result clearly. For example, if an improved prompt increases precision without reducing recall, it aligns with the product goal. If a model upgrade helps across the dataset but introduces new error patterns, it may require further testing. By following these practices, teams can move from promising prototype results to production-ready LLM systems.",
  "summary": "These are the lessons we learned evaluating LLMs for real-world secret scanning. The post How to evaluate LLMs before production appeared first on The GitHub Blog .",
  "key_points": [],
  "editors_take": null,
  "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."
}