The trade-offs between real-time and offline retrieval in QA systems revolve around latency, resource usage, and adaptability. Real-time retrieval processes each query as it arrives, which keeps responses up-to-date but requires immediate computational resources. Offline precomputation reduces query-time work by storing pregenerated answers, but this sacrifices flexibility when data changes. The choice depends on whether freshness, scalability, or consistency is prioritized.
System Design Trade-offs Real-time retrieval demands scalable infrastructure to handle unpredictable traffic. For example, a search engine using real-time retrieval needs distributed systems (e.g., Elasticsearch) to process queries across fresh data. This requires load balancing, caching, and compute resources that scale dynamically, increasing operational complexity and cost. Offline precomputation shifts the burden to preprocessing: generating embeddings or answers during periodic batch jobs. While this simplifies query handling (e.g., lookup from a prebuilt database), it requires significant storage and upfront computation. For instance, a FAQ bot precomputing answers must rerun pipelines whenever source content updates, creating delays between data changes and system accuracy.
Evaluation Challenges Real-time systems are evaluated on latency and accuracy under varying loads. A real-time medical QA tool must balance speed (e.g., sub-second responses) with correctness, which becomes harder during traffic spikes. Offline systems are judged on coverage and staleness. For example, a precomputed legal document retrieval system might score well on common queries but fail on niche terms added after the last update. Metrics like recall and precision must account for how often the precomputed data is refreshed. Offline approaches also struggle with edge cases not anticipated during preprocessing, whereas real-time systems can handle novel queries at the cost of higher compute per request.
Use Case Considerations Real-time retrieval suits applications where data changes rapidly, like news or social media monitoring. For example, a stock market analysis tool needs real-time access to the latest trends. Offline precomputation works for static domains, like historical document archives, where prebuilt indexes reduce costs. Hybrid approaches are common: Wikipedia might precompute answers for high-traffic pages but use real-time search for less common terms. The decision hinges on acceptable response times, budget for infrastructure, and how critical data freshness is to the end user. Developers must weigh these factors against maintenance overhead and scalability risks.