The trade-offs between real-time retrieval and precomputing answers or passages depend on balancing computational costs, latency, flexibility, and maintenance. Real-time retrieval processes each query as it arrives, while precomputing generates answers in advance and serves them from storage. Each approach has distinct implications for system design, user experience, and evaluation.
In system design, real-time retrieval demands scalable infrastructure to handle unpredictable query loads. For example, a search engine using real-time processing might deploy distributed computing frameworks (e.g., Apache Spark) to parallelize tasks like embedding generation or database lookups. This requires ongoing computational resources (e.g., GPU instances) and can increase operational costs during peak traffic. Precomputing, however, shifts costs to an offline phase: generating answers for anticipated queries and storing them in a fast-access database (e.g., Redis). While this reduces runtime costs, precomputing limits coverage to predefined queries and requires assumptions about user intent. For instance, a FAQ bot precomputing answers might miss niche questions, forcing fallback mechanisms like “Sorry, I don’t know.”
Latency and user experience also differ. Real-time systems introduce processing delays (e.g., 200–500ms per query) due to steps like query parsing, retrieval, and ranking. However, they adapt to new or evolving data—a news assistant using real-time retrieval can fetch the latest articles. Precomputed systems offer near-instant responses (e.g., 10ms) by serving cached results, improving perceived speed. The trade-off is rigidity: if a user’s query doesn’t match precomputed patterns, the system fails. For example, a travel guide app with precomputed hotel recommendations might lack updated pricing during a sudden surge in demand.
Evaluation and maintenance further highlight differences. Real-time systems are harder to evaluate because performance depends on live data and diverse queries. Metrics like precision@k must be tracked continuously, and edge cases (e.g., misspelled words) require robust error handling. Precomputed systems simplify evaluation since outputs are fixed and testable upfront, but they demand frequent updates to stay relevant. A weather app precomputing forecasts would need hourly recomputation to maintain accuracy, whereas a real-time system could pull fresh data directly from APIs. Maintenance for precomputed systems also scales with data volatility, making them less suitable for dynamic domains like stock prices or social media trends.
