Skip to main content

Getting Started with LLM Ops

What is LLMOps?​

LLMOps (Large Language Model Operations) is the practice of building, deploying, managing, and improving applications powered by large language models (LLMs) in a reliable, scalable, and governed way.

This site treats LLM Ops as the operational backbone for LLM-powered products: how you ship prompts, retrieval, orchestration, evaluation, observability, cost control, and safety together—not only how you call a model API.

For a layered view of how experience, orchestration, models, data, and governance fit together in production Gen AI systems, see Generative AI Architecture Layers.

Core Principles​

LLM Ops is grounded in a small set of principles that keep LLM-powered software shippable, testable, and maintainable alongside the rest of your stack:

  • Unified release cycles — Unifies the release cycle for LLM-powered applications and traditional software, enabling consistent, reliable delivery across both.
  • Automated testing of LLM artifacts — Enables automated testing of LLM artifacts (e.g., prompt validation, retrieval quality, grounding accuracy, hallucination checks, and agent workflow testing).
  • Agile iteration — Applies agile principles to LLM systems, supporting rapid iteration on prompts, models, retrieval pipelines, and user interactions.
  • First-class CI/CD for LLM assets — Treats prompts, embeddings, retrieval pipelines, and agent workflows as first-class citizens within CI/CD systems, ensuring they are versioned, tested, and deployable.
  • Less technical debt — Reduces technical debt in LLM systems by standardizing prompt lifecycle management, evaluation frameworks, observability, and governance.
  • Vendor- and stack-agnostic — Remains model-, provider-, framework-, and infrastructure-agnostic, enabling portability and flexibility across a fast-evolving LLM ecosystem.

Core Capabilities​

The docs are organized around the same areas highlighted on the site home. Full guides are available for Prompt Management & Versioning. The LLMOps Periodic Table: System Planes has a coming soon overview; every other capability below also uses a coming soon placeholder page (same entries as the sidebar) so navigation stays consistent while detailed documentation is written.

Prompt Management & Versioning​

Treat prompts as versioned artifacts: review changes, roll back, and align templates across environments and teams.

Retrieval-Augmented Generation (RAG) Ops — coming soon​

Operate indexes, chunking, embedding pipelines, and freshness—so answers stay grounded and retrieval quality is measurable.

Model Orchestration, Routing & Agents Ops — coming soon​

Route across models and providers; run intelligent routing and Agents Ops for tool-using, multi-step workflows with guardrails and tracing. See also Agents Ops (coming soon).

Advanced Evaluation — coming soon​

Automate offline and online evals, human review loops, and gates so releases improve quality metrics you actually trust.

AI Observability & Performance — coming soon​

Trace requests end-to-end: latency, errors, token usage, and model outputs—so you can debug production behavior quickly.

Cost Governance & FinOps — coming soon​

Allocate spend by team, product, or tenant; set budgets and alerts on tokens and infrastructure before bills spike.

Guardrails & Security — coming soon​

Enforce content policies, PII handling, access control, and audit trails—reduce abuse and stay aligned with risk and compliance requirements.

The LLMOps Periodic Table: System Planes — coming soon​

Recommended process for leveraging open source, vendor-based, and native technologies as a structured view of production AI system planes.