Streaming

Streaming in AI refers to returning model outputs progressively as they're generated, token by token, rather than waiting for the entire response to complete before sending. This technique transforms user experience for AI applications.

How streaming works:

1. Client initiates request with streaming enabled
2. Server begins generating response
3. Each token is sent immediately when generated
4. Client displays tokens as they arrive
5. Stream closes when generation completes

Benefits of streaming:

• Perceived latency: Users see immediate response
• Time to first token: Often under 500ms vs seconds
• User engagement: Active feedback keeps users engaged
• Cancellation: Users can stop unwanted generations early
• Progressive rendering: Content builds naturally

Implementation considerations:

• Server-Sent Events (SSE) for web applications
• WebSockets for bidirectional communication
• Chunked HTTP responses
• Client-side token accumulation and rendering

When streaming matters most:

• Chat interfaces (ChatGPT experience)
• Real-time assistants
• Long-form content generation
• Interactive applications