To measure the success of intermediate retrieval steps, you need to define evaluation criteria for each stage and establish a connection between the output of one step and the input of the next. This involves verifying that the intermediate results (e.g., a "clue" from the first retrieval) are both relevant to the task and effectively used in subsequent steps. Common approaches include direct evaluation of intermediate outputs, correlation with downstream performance, and controlled experiments to isolate the impact of each step.
One method is to evaluate intermediate outputs using traditional information retrieval metrics like precision, recall, or F1-score for the specific clues they aim to retrieve. For example, if the first retrieval step is designed to identify documents containing a specific keyword or context (e.g., "climate change" in a search for environmental policies), you could manually annotate a subset of data to check if those documents are indeed retrieved. Alternatively, automated checks can validate the presence of expected entities, keywords, or semantic patterns in the intermediate results using tools like regex, named entity recognition, or embedding-based similarity scores. For instance, in a question-answering pipeline, if the first step retrieves a paragraph that should contain a date or name relevant to the final answer, you could measure how often those elements appear in the retrieved text.
Another approach is to measure the downstream impact of intermediate results. For example, compare the final output quality (e.g., answer accuracy or relevance) when using the full pipeline versus a baseline that skips the intermediate step. If the intermediate step is critical, its absence should degrade performance. A/B testing or ablation studies can quantify this. For instance, in a two-stage retrieval system where the first step filters products by category and the second ranks them by user preferences, you could compare conversion rates between a system that uses both stages and one that directly ranks all products. Additionally, metrics like the overlap between intermediate and final results (e.g., how many first-stage documents influence the final ranking) or latency reductions (e.g., faster search due to effective filtering) can indirectly signal success.
Finally, controlled experiments can isolate the effectiveness of intermediate steps. For example, simulate the second retrieval step using ground-truth clues (e.g., manually curated keywords) and compare its performance to when it relies on the first step’s output. This helps determine whether the first step’s results are sufficient for the next stage. In a legal document search system, if the first step aims to identify relevant case law sections, you could replace its output with known relevant sections and measure whether the second step’s accuracy improves. This establishes a clear causal link between the quality of intermediate results and the overall system’s success. Combining these methods provides a comprehensive view of whether each retrieval stage contributes meaningfully to the end goal.