Qiyaasaha Qiimaynta RAG iyo RAGAS

A RAG system can fail in two places: retrieval can fetch the wrong passages, or generation can ignore or distort the right ones. A single overall quality score hides which happened, so RAG evaluation splits the pipeline into measurable parts. RAGAS, an open-source framework introduced in a 2023 paper by Es and colleagues, popularized a set of core metrics that cover both halves. Faithfulness asks whether every claim in the answer is supported by the retrieved context; it catches hallucination even when the answer sounds plausible. Answer relevance asks whether the answer actually addresses the question, penalizing evasive or padded replies. Context precision asks whether the retrieved chunks that matter are ranked near the top rather than buried among noise. Context recall asks whether the retrieved context contains everything needed to produce a reference answer, so it requires a ground-truth answer for each test question. Most of these scores are computed by an LLM acting as a judge, broken into small steps. For faithfulness, the judge extracts individual statements from the answer and checks each against the context; the score is the fraction supported. For answer relevance, RAGAS generates questions that the answer would respond to and compares them with the original question using embeddings. Similar ideas appear in other tools, such as the TruLens RAG triad (context relevance, groundedness and answer relevance) and DeepEval. Two misconceptions are common. First, faithful does not mean correct: an answer can faithfully repeat an outdated or wrong document. Second, LLM-judge scores are not ground truth. They vary with the judge model and prompt, so teams spot-check them against human labels and compare systems under identical settings rather than treating a score like 0.85 as an absolute grade.