Topic
Guardrails & Content Moderation
5 posts on guardrails & content moderation — part of glossary on the n4n AI blog.
How to build guardrails for a production LLM app
A step-by-step guide to implementing input validation, output filtering, and runtime monitoring for LLM applications in production.
How content moderation models classify harmful text
A practitioner's guide to how content moderation models classify harmful text — architecture, taxonomy, thresholds, and production pitfalls.
Guardrails vs content moderation: what's the difference
Understand the technical differences between guardrails and content moderation for LLM systems, with a head-to-head comparison across capabilities, latency, cost, and operational trade-offs.
Guardrails for RAG systems: preventing off-topic answers
A step-by-step guide to implementing guardrails for RAG systems that prevent off-topic answers, with runnable code and verification methods.
AI guardrails explained: keeping LLMs safe and on-topic
What are AI guardrails? A practical breakdown of input/output filtering, system prompts, and moderation layers that keep LLM apps safe, on-topic, and on-brand.
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