Retrieval strategies in RAG systems directly impact interpretability by determining whether answers can be traced to external sources or remain tied to the model’s internal knowledge. When a RAG system cites sources—such as specific documents, databases, or web pages—users gain visibility into the origin of the information, enabling them to verify claims or explore context. This transparency aligns with explainability goals, as it clarifies how the system arrived at an answer. In contrast, answers derived solely from the model’s opaque memory (e.g., a non-retrieval LLM or a RAG system that doesn’t expose sources) lack this traceability, making it harder to assess validity or identify potential biases. For example, a medical RAG system citing peer-reviewed studies allows clinicians to evaluate evidence quality, while a model generating treatment advice without references forces users to trust its internal reasoning without scrutiny.
The choice of retrieval method also influences how developers balance accuracy and transparency. For instance, a RAG system using dense vector retrieval might prioritize semantically relevant passages but struggle to highlight explicit connections between sources and answers, reducing interpretability. Conversely, keyword-based retrieval (e.g., BM25) could surface verbatim text snippets, making citations more directly relatable to the answer. In legal applications, a system retrieving specific case law sections provides clear audit trails, whereas one relying on summarized precedents in its memory might obscure critical nuances. Developers can enhance interpretability by designing retrieval pipelines that prioritize high-quality, verifiable sources and explicitly link them to generated answers, even if this requires trade-offs in response fluency or latency.
Evaluating user trust involves both subjective and objective measures. Surveys can quantify perceived trust by asking users to rate answers with and without cited sources on reliability and transparency. A/B testing can reveal behavioral differences, such as whether users fact-check cited answers more often or accept them at face value. Additionally, tracking metrics like error rates in cited sources (e.g., incorrect references) or user engagement (e.g., follow-up queries for clarification) provides insight into trust dynamics. For example, a study might show that users trust sourced answers more initially but lose trust if citations are irrelevant or inaccurate. Combining these methods helps developers understand how retrieval strategies affect trust and where improvements—like better source validation or clearer attribution—are needed.