{
  "id": 19115509,
  "title": "How do you evaluate the quality and accuracy of responses generated by an LLM?",
  "url": "https://urgent.news/2026/11/09/how-do-you-evaluate-the-quality-and-accuracy-of-responses-generated",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-09T08:01:37.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/technaom/how-do-you-evaluate-the-quality-and-accuracy-of-responses-generated-by-an-llm-3pem"
  },
  "original_language": "en",
  "account": "When assessing the quality and accuracy of responses from a language model (LLM), one must move beyond a single accuracy metric and construct a comprehensive evaluation pipeline. This involves creating a golden dataset comprising representative queries, reference answers, rubrics, and expected source chunks or memory items.\n\nBegin by testing the retrieval component separately. Measure context precision and recall against a set of labeled relevant chunks. For the generation model, assess faithfulness—ensuring claims are supported by the provided context—as well as answer relevance and correctness by comparing them to references. In the case of agent-based models with memory, scrutinize the retrieval process: verify that the correct memory was accessed, that it is recent enough, and that any truncation or summarization does not omit crucial facts.\n\nConsider utilizing the LLM itself as a judge for scalability. However, it is crucial to calibrate this judge against human-labeled samples, locking in the model and prompt version to maintain consistency. Report agreement rates to gauge the reliability of the automated evaluation.\n\nFor deterministic checks, employ schema validation, exact match comparisons, and citation presence where applicable. Incorporate offline evaluations into continuous integration (CI) processes to run every time changes are made to prompts, models, or chunking schemes. Concurrently, score a sample of live traffic online to monitor drift, user feedback, and escalation rates.\n\nWhen tying these metrics to business outcomes, recognize that a higher faithfulness score does not automatically equate to a superior product. Beware of common pitfalls such as relying on aggregate scores or limited manual spot checks, which can obscure regressions in specific query types. Also, avoid using the same model for both generation and judgment, as this can introduce bias through shared learning. Lastly, evaluate the entire answer generation process, not just the final output—failing to do so may mask issues like retrieval errors, stale memory, or context overflow that present as hallucinations.\n\nPrioritize checking for the failure of proper memory retrieval first when designing evaluations for agents with session memory.",
  "summary": "Asked in a GenAI Engineer interview. What they are really testing: Whether you can turn a vague idea of a \"good answer\" into a repeatable evaluation pipeline that also covers retrieved context and agent memory, not just a single accuracy number. A strong answer: Start with a golden dataset of representative queries, reference answers or rubrics, and the expected source chunks or memory items.…",
  "key_points": [
    "Create a golden dataset with representative queries and reference answers",
    "Assess faithfulness, answer relevance, and correctness against references",
    "Use LLM as judge, calibrate against human-labeled samples for scalability"
  ],
  "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."
}