LLMOps & Production
Serving and operating LLMs in production — gateways, routing, fallback and retry, load testing, context-window management, observability, cost control, logging, A/B testing, and model registry.
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What topics does this domain cover?
10 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
A/B Testing for LLMs →
A/B testing for LLMs runs controlled experiments that compare two or more prompt versions, model configurations, or …
Context Window Management →
Context window management encompasses the techniques used to fit relevant information within an LLM's fixed token limit …
LLM Cost Management →
LLM Cost Management covers the strategies and tooling used to control operational expenses in LLM-powered systems. It …
LLM Fallback and Retry Patterns →
LLM fallback and retry patterns are resilience strategies that keep AI-powered applications running when a model …
LLM Gateway →
An LLM Gateway is an API management layer that sits between your application and one or more LLM providers. It handles …
LLM Load Testing →
LLM load testing measures how an AI system performs under realistic traffic — tracking tokens-per-second output, …
LLM Logging and Auditing →
LLM Logging and Auditing covers production practices for capturing, storing, and analyzing prompt/response pairs in LLM …
LLM Observability →
LLM Observability is the practice of monitoring, tracing, and debugging large language model applications in production. …
Model Registry →
A model registry is the often-overlooked bridge between training and production: it enforces that every deployed model …
Model Routing →
Model routing is the practice of dynamically directing each LLM request to the most appropriate model based on query …