LLMOps Cheat Sheet

Updated 2026-04-20

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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 ConceptsTable 2: Prompt Management & EngineeringTable 3: Model Fine-Tuning & AdaptationTable 4: Deployment & ServingTable 5: Retrieval-Augmented Generation (RAG)Table 6: Monitoring & ObservabilityTable 7: Evaluation & TestingTable 8: Hallucination Detection & MitigationTable 9: Security & SafetyTable 10: Cost OptimizationTable 11: Data Management & PreprocessingTable 12: CI/CD & AutomationTable 13: Error Handling & ResilienceTable 14: Agent Orchestration & Multi-Agent SystemsTable 15: Governance & ComplianceTable 16: Advanced Optimization TechniquesTable 17: LLMOps Platforms & Tools

Table 1: Core LLMOps Concepts

Concept	Example	Description
LLMOps	Deploying ChatGPT-like apps in production	Practices for developing, deploying, and managing LLMs throughout their lifecycle
Prompt Engineering	Crafting instructions for specific output format	• Designing input prompts to guide LLM behavior • prompts directly impact quality and cost
Context Engineering	Dynamic retrieval + compression + memory	Systematically controlling what information the LLM sees at inference time to maximize output quality
Fine-Tuning	Adapting GPT for legal document analysis	Training a pre-trained LLM on domain-specific data to improve performance on specialized tasks
Inference Optimization	Using quantization + batching to reduce latency	Techniques to speed up LLM predictions and reduce computational cost during serving
RAG (Retrieval-Augmented Generation)	Answering questions using external docs	Combining retrieval from a knowledge base with generation to ground responses in facts
Observability	Tracing every LLM call with metadata	Monitoring LLM applications via logging, tracing, and metrics to detect issues and optimize performance

Table 1: Core LLMOps Concepts

Concept	Example	Description
LLMOps	Deploying ChatGPT-like apps in production	Practices for developing, deploying, and managing LLMs throughout their lifecycle
Prompt Engineering	Crafting instructions for specific output format	• Designing input prompts to guide LLM behavior • prompts directly impact quality and cost
Context Engineering	Dynamic retrieval + compression + memory	Systematically controlling what information the LLM sees at inference time to maximize output quality
Fine-Tuning	Adapting GPT for legal document analysis	Training a pre-trained LLM on domain-specific data to improve performance on specialized tasks
Inference Optimization	Using quantization + batching to reduce latency	Techniques to speed up LLM predictions and reduce computational cost during serving
RAG (Retrieval-Augmented Generation)	Answering questions using external docs	Combining retrieval from a knowledge base with generation to ground responses in facts
Observability	Tracing every LLM call with metadata	Monitoring LLM applications via logging, tracing, and metrics to detect issues and optimize performance