When comparing RAG systems beyond correctness, relevance and context handling are critical. A strong system should stay focused on the query’s intent without veering into unrelated topics. For example, if asked, “How does climate change affect agriculture?” a response that diverges into general climate science without addressing farming impacts is less useful. Additionally, multi-turn interactions require maintaining context. If a user follows up with, “What about drought-resistant crops?” the system should recognize the connection to the original question. Ambiguous queries, like “What’s the best approach?” without context, test the system’s ability to clarify or infer intent, rather than guessing incorrectly. Relevance also includes prioritizing the most useful information—answering “How do neural networks work?” with overly technical jargon might miss the user’s knowledge level.
Next, depth and coherence determine how well the answer addresses complexity. A shallow response to “Explain quantum computing” might list basic terms without connecting them, while a deeper answer would outline qubits, superposition, and real-world applications. Coherence refers to logical flow: even a correct answer that jumps between ideas or lacks structure (e.g., mixing definitions and examples haphazardly) is harder to follow. For technical topics like “RAG architecture,” the system should distinguish between retrieval and generation phases clearly. Depth also involves recognizing when a question requires step-by-step reasoning versus a high-level summary, such as explaining a programming concept to a beginner versus an expert.
Finally, source quality and bias impact trustworthiness. A system citing outdated or unreliable sources (e.g., an unverified blog post for medical advice) raises red flags, even if the answer seems correct. Diversity of sources matters too—relying solely on one domain (e.g., only academic papers for a pop culture question) can skew results. Bias evaluation includes checking for unintended stereotypes, like assuming a “CEO” is male. Transparency about sources (e.g., citing Wikipedia vs. peer-reviewed journals) helps users assess credibility. For example, a response about climate policy should balance perspectives without overemphasizing fringe theories, ensuring fairness and factual grounding.