AI Engineering / LLMOps

This topic follows an application through its production lifecycle to answer one question: what engineering capabilities does an LLM application need to move from a working demo to reliably serving real traffic? LLMOps can include training and self-hosting; here the focus is request management, evaluation, observability, releases, costs, incidents, and feedback. For training methods, see LLM; for application architecture, see Agent and RAG.

Example thresholds are not industry standards, and model snapshots are not model-selection recommendations. Established distributed-systems methods are discussed separately from the additional constraints of generative applications.

Modules

1. Foundations and Production Architecture (Chapters 1–2)
2. Request-Path Reliability (Chapters 3–4)
3. Output Quality and Safety (Chapters 5–6)
4. Evaluation and Observability (Chapters 7–8)
5. Versioning and Release Pipelines (Chapters 9–10)
6. Performance, Cost, and Operations (Chapters 11–13)

How the modules relate

flowchart TB
    F[Foundations and Production Architecture] --> RR[Request-Path Reliability]
    F --> OS[Output Quality and Safety]
    RR --> EO[Evaluation and Observability]
    OS --> EO
    EO --> RP[Versioning and Release Pipelines]
    RR --> PO[Performance, Cost, and Operations]
    RP --> PO
    PO -.Production feedback.-> EO

Request-Path Reliability and Output Quality and Safety address two aspects of the same layer: whether a call can complete, and whether its result can be trusted. Their signals feed Evaluation and Observability. Evaluation results then determine whether Versioning and Release Pipelines can approve a change. Performance, Cost, and Operations keeps the deployed system running reliably and feeds production feedback back into evaluation.

LLMOps, MLOps, and DevOps: a guide to their scope

These terms are often used interchangeably, but they emphasize different concerns. See Chapter 1:

	Core assets	Typical question
DevOps	Application code	How do we reliably build, test, and deploy code to production?
MLOps	Data, models, and ML pipelines	How do we manage data, training, evaluation, serving, and distribution shifts—not only for models we train ourselves?
LLMOps	Models, prompts, context, tools, and routing	How do we extend existing engineering practices to handle open-ended quality, nondeterministic generation, and limits on execution, for both hosted and self-hosted systems?

Relationship to other topics

Material covered elsewhere is cross-referenced rather than repeated:

Concept	Detailed coverage	How this topic uses it
Model deployment, batching, KV caching, quantization	LLM · Inference and Serving	Chapter 11 covers application-level caching and batching strategies without repeating inference-engine internals
The seven core LLM gateway capabilities and gateway selection	Tools · Transport and Gateways	Chapter 3 focuses on routing policies and fallback design; building the gateway itself is covered in Tools
Implementing the LangSmith production quality feedback loop	LangChain · Production Practices	Chapter 8 presents a vendor-neutral observability model; LangSmith is one implementation
Agent evaluation and safety	Agent · Evaluation and Safety	Chapter 7 covers general evaluation methods; agent-specific tool-call and multi-turn evaluation are covered in Agent
General-capability evaluation metrics such as MMLU	LLM · Evaluation and Model Selection	Chapter 7 covers application evaluation workflows; academic benchmarks are covered in LLM

Suggested reading paths

• New to LLMOps: Foundations and Production Architecture → Evaluation and Observability → Versioning and Release Pipelines.
• Responsible for production incidents: Request-Path Reliability → Output Quality and Safety → Performance, Cost, and Operations.
• Building evaluation and release processes: Evaluation and Observability → Versioning and Release Pipelines.
• Managing cost and capacity: Performance, Cost, and Operations, alongside LLM · Inference and Serving.

For interview preparation, follow one concrete request: does a timeout mean the action was not executed? Does valid JSON authorize placing an order? Is a higher average score enough to approve a release? Does the fallback model satisfy data-residency requirements? Can rollback undo actions already taken? Explain denominators, versions, permissions, and failure handling rather than merely naming components. If you lack real project experience, present an example as a design proposal or experiment; do not claim this topic’s example numbers as personal achievements.

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