LLM Safety
Noun · AI & Machine Learning
Definitions
LLM Safety is a safety or governance control for limiting harmful, noncompliant, or insecure model behavior. It is commonly used for production AI systems that need policy enforcement and auditability, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to coverage, false positives, and adversarial pressure, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.
In plain English: LLM Safety is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.
Example: "We added LLM Safety to the assistant stack so prompts stayed within the context window, outputs became more consistent, and the inference path stopped failing on long enterprise documents during peak traffic."
Related Terms
- Token
- Causal Language Model
- LLM Agent
- LLM Evaluation
- LLM Fine-Tuning
- LLM Inference
- LLM Routing
- Local LLM
- Long Context
- Multimodal Tokenizer
- OpenAI
- Output Token
- Prompt Caching
- Prompt Chaining
- Prompt Engineering Detail
- Prompt Injection Detail
- Prompt Template
- Prompt Tuning
- Stop Token
- Subword Tokenization
- System Prompt Detail
- Token Budget
- Token Limit
- Tokenization Detail
- Tokens Per Second Detail
- Zero-Shot Prompting