Principle:Googleapis Python genai Content Generation
| Knowledge Sources | |
|---|---|
| Domains | NLP, Generative_AI |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
The core inference operation that transforms input content into generated text by invoking a large language model.
Description
Content Generation is the fundamental operation in generative AI: given an input sequence of tokens (text, images, or other modalities), a language model produces a continuation or response. This principle covers both unary (complete response returned at once) and streaming (response chunks delivered incrementally) modes. The operation involves encoding the input, running forward inference through the model, applying sampling strategies to decode output tokens, and packaging the result with metadata (token counts, safety ratings, finish reason).
Usage
Use content generation whenever you need to obtain model-generated text from input prompts. Choose unary mode when you need the complete response before processing (e.g., structured JSON output, batch processing). Choose streaming mode when displaying results to users in real-time (e.g., chatbots, interactive applications) to reduce perceived latency.
Theoretical Basis
Autoregressive language model generation works by iteratively predicting the next token:
Where x is the input context and y is the generated sequence. Each token is sampled according to the configured decoding strategy (temperature, top-p, top-k).
Streaming delivers partial results by yielding tokens (or token groups) as they are generated, rather than waiting for the complete sequence:
# Pseudo-code for streaming generation
for token in model.generate(input):
yield partial_response(token)