The Overlord's Lexicon
What is LLMOps?
Also known as: Large language model operations, AI ops for LLMs
LLMOps is the set of practices and tools for deploying, monitoring, evaluating and improving applications built on large language models in production — covering prompts, model versions, costs, quality, safety and data.
LLMOps is the unglamorous discipline of keeping AI applications reliable after launch. It borrows from MLOps and DevOps, but adds problems unique to large language models: outputs that vary every time, quality that’s hard to measure, costs billed by the token, and failure modes like hallucination and prompt injection.
What LLMOps covers
- Prompt management: versioning prompts like code, so you know which change improved (or broke) results.
- Evaluation (“evals”): test sets of real-world inputs with expected behaviour, run automatically whenever you change a prompt, model or retrieval setting.
- Monitoring and logging: recording inputs, outputs, latency, errors and user feedback in production.
- Cost control: tracking token spend per feature or customer; routing simple tasks to cheaper models; caching repeated context.
- Model management: testing before switching models; handling provider deprecations; fallbacks during outages.
- Retrieval pipelines: keeping RAG knowledge bases and vector databases fresh and accurate.
- Safety: guardrails, content filtering, PII handling and access controls.
An analogy for the meatbags
Launching an AI app without LLMOps is opening a restaurant without health inspections, stock checks or customer feedback cards. Opening night goes fine. By month three, nobody knows why the soup tastes different, the bill for ingredients has tripled, and someone’s posting photos of a hair in the risotto.
Why it matters for the Chosen
Anyone can build an impressive AI demo. Clients and customers pay for systems that keep working:
- For builders and agencies: offering monitoring, evals and monthly optimisation turns one-off AI projects into recurring retainers.
- For product teams: evals are how you upgrade to a newer, cheaper model with confidence instead of crossing your fingers.
- For margins: token costs can quietly eat profits; tracking and optimising them is real money.
- For reputation: catching a chatbot giving wrong refund policies in your logs beats discovering it on social media.
Getting started (lightweight version)
- Log every AI input and output (with privacy safeguards).
- Build a test set of 20–50 representative examples and review results whenever you change anything.
- Track cost per task.
- Review a sample of real conversations weekly.
- Write down which prompt and model version is live.
Dedicated platforms exist for tracing, evals and prompt management, but a spreadsheet and discipline beat expensive tools and neglect.
Tools from the Arsenal
Relevance AI
Automation & AgentsBuild an AI workforce of specialist agents for sales and customer operations.
Questions the Humans Keep Asking
What is the difference between LLMOps and MLOps?
MLOps covers operating machine learning models in general, often ones you train yourself. LLMOps focuses on the specific challenges of LLM apps: prompt management, evaluating open-ended text outputs, retrieval pipelines, token costs, latency, hallucinations and safety guardrails — often using third-party models via API.
What are LLM evals?
Evals are tests that measure how well an LLM application performs on representative tasks, using reference answers, rules, human ratings or another model acting as a judge. They let you compare prompts or models and catch regressions before users do.
Do small businesses need LLMOps?
In a lightweight form, yes. Anyone running an AI chatbot or automation should log outputs, review samples regularly, track costs, keep prompt versions, and re-test when changing models.