Having a variety of question types in a RAG (Retrieval-Augmented Generation) evaluation set is critical because it mirrors real-world user interactions and tests the system’s ability to handle diverse information needs. Different question types target distinct components of the RAG pipeline—retrieval accuracy, comprehension, and generation quality—ensuring the system isn’t overfit to a single task. For example, a user might ask for a simple fact, a detailed explanation, or a yes/no confirmation, and the system must adapt to each scenario. By including multiple question types, developers can identify weaknesses in specific areas, such as retrieving precise details versus synthesizing broader concepts, leading to more balanced improvements.
Factoid questions (e.g., “When was Python first released?”) stress the retrieval component by demanding precise answers from a large corpus. The system must locate the correct document or passage and extract the exact date, name, or entity. Poor retrieval here could stem from inadequate indexing, sparse context, or ambiguous phrasing in the source data. For instance, if multiple documents mention Python’s release in different contexts (e.g., programming language vs. snake species), the system might retrieve irrelevant information. This highlights the need for robust entity disambiguation and ranking algorithms.
Explanatory questions (e.g., “How does a neural network work?”) challenge both retrieval and generation. The system must first gather relevant fragments from multiple sources (e.g., training processes, activation functions, layer architectures) and then synthesize them into a coherent, logically structured explanation. Weaknesses here might include retrieving incomplete or contradictory information or failing to organize concepts hierarchically. For example, if the retrieval phase misses key details about backpropagation, the generated explanation would lack depth, even if the language model is strong.
Boolean questions (e.g., “Is Python a statically typed language?”) test the system’s ability to infer intent and make binary decisions. These questions require the system to validate a claim against retrieved evidence, which depends heavily on semantic understanding. A “no” answer demands confidence that no supporting evidence exists in the corpus. If the system retrieves a passage stating, “Python uses dynamic typing,” but misses the explicit negation of static typing, it might incorrectly answer “yes.” This stresses the need for precise retrieval and the model’s ability to reason about absence of information.
In summary, varied question types expose different failure modes: factoid questions test precision, explanatory questions assess synthesis, and boolean questions evaluate reasoning. A robust evaluation set ensures the RAG system is tested holistically, guiding improvements across retrieval, comprehension, and generation stages.