Splitting an evaluation into retrieval and generation components using the same dataset provides clarity in diagnosing system performance and optimizing each component independently. By separating these stages, developers can identify whether failures stem from the system’s ability to find relevant information (retrieval) or its capacity to synthesize answers from that information (generation). This isolation prevents conflating issues, which is common in end-to-end evaluations where poor generation might mask strong retrieval, or vice versa. For example, if a question-answering system performs poorly overall, evaluating retrieval first reveals whether correct answers exist in the retrieved documents. If retrieval succeeds but generation fails, the problem lies in how the model processes the data, not its ability to find it.
Using the same dataset for both evaluations ensures consistency and reduces resource overhead. For instance, in a QA system, the same set of questions and documents can first be tested to measure retrieval accuracy (e.g., “Does the correct answer appear in the top 3 retrieved passages?”). If retrieval achieves 90% accuracy but end-to-end generation only reaches 60%, developers know to focus on improving the generator—such as refining prompt engineering or adjusting output constraints. Conversely, if retrieval accuracy is low, efforts can shift to enhancing embedding models or document indexing. This approach avoids the need for separate datasets and ensures both components are tested under identical conditions, making comparisons fair and actionable.
Structuring evaluations this way also streamlines iterative development. Teams can parallelize improvements: one group optimizes retrieval by testing different embedding algorithms, while another fine-tunes the generator’s ability to extract answers. For example, a developer might discover that a retrieval model misses key terms due to poor preprocessing, while the generator struggles with multi-step reasoning even when documents are relevant. By decoupling these stages, targeted fixes—like adding synonym handling in retrieval or chain-of-thought prompting in generation—become easier to implement and validate. This modularity accelerates debugging and fosters a more systematic approach to building robust systems.