Batch Processing: Definition and Examples

Batch processing is a fundamental technique in artificial intelligence that allows executing a large number of requests in a single grouped operation. Instead of sending each prompt individually and waiting for the response before moving on, a batch of requests is assembled and processed together in an optimized manner.

In the context of LLMs and prompt engineering, batch processing is particularly useful when the same treatment needs to be applied to many inputs: translating hundreds of texts, classifying thousands of comments, or generating descriptions for a product catalog. API providers like Anthropic or OpenAI offer dedicated batch processing endpoints, often with reduced rates (up to 50% savings) and longer but predictable processing times.

The main benefit of batch processing lies in cost and resource optimization. By grouping requests, the system can better plan computing power usage, resulting in lower unit prices. It also saves the developer considerable time, as they no longer need to manually handle request loops with error management and rate limiting.

Batch processing contrasts with real-time processing, where each request is handled immediately. The choice between the two depends on the use case: if the user expects an instant response, real-time is necessary. If the result can wait a few hours, batch is almost always preferable for economic and reliability reasons.