How Query Caching/Prefetching Improves Apparent Efficiency Query caching or prefetching in a RAG system reduces the need to repeatedly process identical or similar queries through the vector store and generative components. By storing precomputed results for frequently asked questions (FAQs), the system bypasses time-consuming steps like embedding generation, vector similarity searches, and LLM inference. For example, in a customer support chatbot, if 30% of queries relate to return policies, caching those responses allows instant answers instead of reprocessing them. This lowers latency for cached queries, making the system appear faster and more scalable, even if the underlying vector store’s raw performance remains unchanged. It also reduces computational load on the vector database, freeing resources for uncached queries.
Pros of Evaluating with Caching Enabled Evaluating a RAG system with caching enabled provides insights into real-world performance, as users often repeat questions. Metrics like average response time and throughput improve, reflecting practical benefits. For instance, a 50% cache-hit rate could halve latency for common queries, making the system viable for high-traffic applications. Testing with caching also reveals tradeoffs, such as cache invalidation strategies (e.g., time-based vs. event-driven refreshes) and memory usage. Additionally, it highlights whether prefetching heuristics (e.g., predicting trending topics) align with actual user behavior, which is critical for optimizing cache utility.
Cons of Evaluating with Caching Enabled Caching can mask underlying inefficiencies. For example, a system with poor retrieval accuracy might still score well in evaluations if most test queries are cached, hiding flaws in the vector store or LLM components. Metrics like recall or answer quality for uncached ("cold") queries become harder to isolate, skewing performance assessments. Caching also introduces complexity: tests must account for cache-hit ratios, staleness (e.g., outdated answers after data updates), and overhead from cache management. If the evaluation dataset lacks query diversity or overrepresents FAQs, results won’t reflect real-world scenarios where novel queries dominate. For example, a medical RAG system caching common symptom checks might perform poorly on rare conditions not in the cache, but evaluations focused on FAQs would miss this gap.
In summary, caching improves perceived efficiency by accelerating common queries but complicates evaluations by conflating cached and uncached performance. Effective testing requires isolating metrics for both scenarios and ensuring the dataset reflects real-world query patterns.