Different pipeline architectures

Beyond just the integration pattern itself, the decision between real-time and batch processing has major implications for the surrounding data pipelines and infrastructure architecture. Pre-processing and post-processing workflows take on very different characteristics optimized for their respective modes.

For real-time, low-latency use cases such as query answering or conversational AI, lightweight just-in-time pre-processing pipelines are ideal. These handle prompt cleanup, context augmentation, and other steps with minimal overhead before hitting the generative model with a single inference request. The output then flows through a post-processing stage focused on safety filtering, response ranking, and result formatting. These processes need to be optimized because end-to-end latency is critical.

Real-time pipelines are typically hosted on dynamically scalable containerized infrastructure or serverless cloud environments. Aggressive caching...