To measure the success of intermediate retrieval steps, such as verifying whether a clue from the first retrieval aids the second, you need a combination of direct evaluation metrics and downstream performance analysis. First, evaluate the intermediate outputs independently using metrics like precision, recall, or relevance scoring. For example, if the first retrieval aims to find a set of keywords or documents that guide the second step, you can check if those results align with predefined ground truth clues. If the system retrieves a document containing a specific date or name needed for the next step, you could calculate how often those critical pieces are correctly identified. Tools like exact match checks, keyword overlap scores, or semantic similarity metrics (e.g., cosine similarity between retrieved text and expected clues) can quantify this.
Next, assess how the intermediate results impact the final output. For instance, if the second retrieval uses the clue to narrow down a database query, track whether the final answer improves when the intermediate step is successful. A/B testing is useful here: compare the system’s end-to-end performance with and without the intermediate step, or with different retrieval strategies. Suppose a question-answering system first retrieves a supporting paragraph (clue) and then extracts an answer from it. If the paragraph is relevant, the answer accuracy should increase. By correlating intermediate success (e.g., paragraph relevance scores) with final accuracy, you can validate the importance of the clue.
Finally, use structured validation checkpoints. For example, in a multi-hop question-answering pipeline, manually annotate intermediate clues expected at each step and measure retrieval accuracy at each stage. If the first step should retrieve “events in 1969” to answer "Who won the World Series that year?", verify whether the retrieved documents include 1969 baseball results. Logging intermediate outputs during testing and analyzing failure modes (e.g., the second step failing due to missing clues) also helps identify bottlenecks. Tools like confidence scores for retrieved items or error attribution frameworks can isolate where the pipeline breaks down, ensuring measurable validation of each step’s contribution.
