To determine the number of retrieval rounds in a multi-step system, you balance answer quality, computational cost, and user experience. A common approach is to set a fixed maximum depth (e.g., 3–5 steps) based on empirical testing, domain complexity, and acceptable latency. For example, a medical QA system might allow more steps than a trivia bot due to higher stakes. Beyond this threshold, additional steps often yield minimal improvements while increasing costs and delays. To measure diminishing returns, track metrics like the percentage of new relevant information added per step or the stability of the answer (e.g., how much the system’s output changes with each retrieval). If a step contributes less than a predefined threshold (e.g., <5% new relevant content), further retrievals are halted.
Diminishing returns can be quantified using task-specific metrics. For fact-based queries, measure precision/recall of retrieved documents at each step. For generative tasks, compute the similarity between outputs generated with N vs. N+1 retrieval steps (e.g., using BERTScore or ROUGE). If similarity exceeds a threshold (e.g., 95%), additional steps are unlikely to help. Tools like confidence scores from LLMs (e.g., the model’s certainty in its answer) or query expansion failure rates (e.g., rephrased queries retrieving redundant content) can also signal when to stop. For instance, if a system retrieves the same documents across multiple rewritten queries, further steps add little value.
Practical implementation involves adaptive termination. Start with a baseline (e.g., 3 steps) and use A/B testing to compare answer quality, latency, and user satisfaction. For dynamic adjustment, employ rules like stopping when two consecutive retrievals return overlapping content (measured via Jaccard similarity or embedding-based clustering). In code, this might involve a loop that breaks early if new_docs_similarity > 0.8 or confidence_score > 0.9. Open-source frameworks like LangChain or LlamaIndex provide built-in iterators (e.g., AutoMergingRetriever) that automate depth decisions by merging redundant context, offering a practical starting point for developers.