LLM Routing
Noun · AI & Machine Learning
Definitions
LLM Routing is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research pipelines, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to data fit, compute cost, and reliability, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.
In plain English: LLM Routing 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 Routing 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 Safety
- 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