AI Glossarytechniques
Batch Processing
Processing multiple AI requests together as a group, typically at lower cost and higher throughput than real-time individual requests.
How It Works
Batch processing sends many prompts to an AI model at once rather than one at a time. OpenAI's Batch API offers 50% cost savings for requests that can tolerate up to 24-hour completion times. This is ideal for tasks like processing a dataset, generating content in bulk, or running evaluations.
The tradeoff is latency: batch requests are queued and processed when capacity is available, so you cannot use them for real-time user interactions. But for backend tasks like nightly report generation, bulk classification, content pre-generation, or model evaluation, batch processing saves significant money.
In practice, batch processing works well alongside real-time inference. Use real-time for user-facing features (chat, search, analysis) and batch for background operations (re-indexing embeddings, generating weekly summaries, evaluating model quality across test sets). Most production AI systems use both modes.
Common Use Cases
- 1Bulk content generation
- 2Dataset classification and labeling
- 3Model evaluation across test sets
- 4Nightly data processing pipelines
- 5Cost optimization for non-urgent tasks
Related Terms
Large Language Model (LLM)
A neural network trained on massive text datasets that can generate, understand, and reason about human language.
InferenceThe process of running a trained AI model to generate predictions or outputs from new inputs, as opposed to training the model.
StreamingA method of receiving AI model output token-by-token in real time as it is generated, rather than waiting for the complete response.
LatencyThe time delay between sending a request to an AI model and receiving the response, critical for real-time user-facing applications.
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