Topic
Streaming Responses & Server-Sent Events
5 posts on streaming responses & server-sent events — part of glossary on the n4n AI blog.
What is streaming in an LLM API?
Streaming in LLM APIs delivers tokens incrementally via Server-Sent Events, reducing perceived latency and enabling real-time UX patterns.
Server-sent events explained for LLM APIs
A practical guide to server-sent events for LLM APIs — how SSE works, why it beats polling and WebSockets for streaming tokens, and what to watch for in production.
How to stream OpenAI-compatible chat completions
Learn to stream OpenAI-compatible chat completions with Server-Sent Events, including request format, chunk parsing, error handling, and verification steps.
How to parse a streaming chat completion in Python
Learn to parse streaming chat completions in Python with a complete, runnable SSE parser that handles chunked JSON, tool calls, and provider quirks.
How streaming affects perceived latency in chat UIs
How streaming changes perceived latency in chat UIs, with concrete patterns for token-by-token rendering, cancellation, and backpressure handling.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88