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Plain-language AI glossary

Term 31TechniquesMeaning / context / connections

Techniques / Definition

Batch Processing

Processing multiple AI requests together as a group, typically at lower cost and higher throughput than real-time individual requests.

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MeaningThe one-sentence definition.

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ContextHow the idea works in practice.

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01 / Plain-language context

How Batch Processing 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.

02 / Practical uses

Where it helps.

  1. 01Bulk content generation
  2. 02Dataset classification and labeling
  3. 03Model evaluation across test sets
  4. 04Nightly data processing pipelines
  5. 05Cost optimization for non-urgent tasks
03 / Keep exploring
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.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.