How do different retrieval strategies affect the interpretability or explainability of a RAG system’s answers (for example, an answer with cited sources vs. an answer from an opaque model memory), and how might you evaluate user trust in each approach?

Different retrieval strategies in RAG systems directly impact the interpretability of answers by determining whether the system provides transparent sourcing or relies on opaque internal knowledge. When a RAG system retrieves and cites external sources (e.g., documents, databases), users can trace the origin of the information, making the answer more explainable. For example, if a user asks, “What causes climate change?” and the system cites peer-reviewed studies or authoritative reports, the answer’s credibility is reinforced by visible evidence. In contrast, a non-RAG model (like a standard language model) generates answers solely from its pre-trained memory, offering no way to verify where the information came from. This lack of sourcing makes it harder for users to assess accuracy, especially for nuanced or contested topics. Retrieval-based systems thus prioritize transparency, while opaque models trade explainability for simplicity.

Evaluating user trust in these approaches requires measuring both perceived reliability and actual verification behavior. For systems with cited sources, trust can be assessed through user surveys asking how confident respondents feel about answers when sources are visible. Metrics like click-through rates on citations or time spent reviewing linked documents can indicate engagement with sourcing. For opaque models, trust might correlate with the system’s historical accuracy—for example, tracking how often users accept answers without questioning them. A/B testing can compare trust levels by presenting the same answer with and without citations. Additionally, domain-specific evaluations (e.g., medical or legal contexts) can measure trust by involving experts to validate answers against ground truth. Over time, systems that consistently provide accurate, well-sourced responses will likely build stronger trust, even if users don’t fully understand the retrieval mechanism.

Specific examples highlight these differences. A RAG system for technical documentation might retrieve code snippets from official API references and link to them, allowing developers to confirm the advice aligns with the latest version. In contrast, a language model generating code from memory could inadvertently suggest deprecated methods, with no way for users to spot the issue. Similarly, in healthcare, a RAG system citing clinical trials enables doctors to validate recommendations, while an opaque model’s unsourced claims might be dismissed as unreliable. To evaluate trust, developers could simulate high-stakes scenarios (e.g., debugging critical errors) and measure how often users double-check cited sources versus accepting opaque answers. These tests reveal whether transparency directly influences user confidence and decision-making, providing actionable insights for improving system design.