LLMOps (Large Language Model Operations) is the specialized discipline of deploying, managing, monitoring, and maintaining large language models in production environments. It extends traditional MLOps practices while addressing unique challenges of LLMs, including prompt engineering, inference optimization, hallucination detection, and massive computational requirements. Unlike classic ML pipelines, LLMOps must handle non-deterministic outputs, context window constraints, costly API calls, and the rapid evolution of model capabilities—making observability, cost control, and iterative experimentation central to every deployment. In 2026, the field has matured around agentic workflows, standardized protocols like MCP and A2A, and a growing emphasis on governance as regulations such as the EU AI Act take full effect.
What This Cheat Sheet Covers
This topic spans 17 focused tables and 204 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core LLMOps Concepts
The vocabulary every LLMOps practitioner reaches for daily — from prompt and context engineering to RAG, guardrails, and gateways. Get these mental models straight first and the rest of the cheat sheet clicks into place, because nearly every later table is just a deeper dive into one of these terms.
| Concept | Example | Description |
|---|---|---|
Deploying ChatGPT-like apps in production | Practices for developing, deploying, and managing LLMs throughout their lifecycle | |
Crafting instructions for specific output format | • Designing input prompts to guide LLM behavior • prompts directly impact quality and cost | |
Dynamic retrieval + compression + memory | Systematically controlling what information the LLM sees at inference time to maximize output quality | |
Adapting GPT for legal document analysis | Training a pre-trained LLM on domain-specific data to improve performance on specialized tasks | |
Using quantization + batching to reduce latency | Techniques to speed up LLM predictions and reduce computational cost during serving | |
Answering questions using external docs | Combining retrieval from a knowledge base with generation to ground responses in facts | |
Tracing every LLM call with metadata | Monitoring LLM applications via logging, tracing, and metrics to detect issues and optimize performance |