Index-Time RAG vs Real-Time RAG: Choosing the Right Retrieval Strategy

Retrieval-augmented generation (RAG) systems, which combine a language model with external context, face a critical architectural decision between index-time and real-time retrieval strategies, each with distinct tradeoffs in terms of latency, cost, accuracy, and compliance. Index-time RAG involves pre-indexing data for fast and predictable query responses but risks delivering outdated information if the index isn't kept current, while real-time RAG retrieves data on-demand from source systems, ensuring up-to-date results but potentially higher latency and costs. Hybrid models are often adopted in enterprise SaaS environments to balance these tradeoffs, using index-time retrieval for stable content and real-time retrieval for dynamic, permission-sensitive data. This approach is crucial for maintaining accuracy and trust in AI features, especially in environments with high data churn, fine-grained permissions, and operational risk. Unified, a platform designed around these principles, supports both strategies, allowing teams to maintain compliance and correctness by accessing SaaS data through real-time, authorized API calls, and keeping indexed content current with event-driven updates.