Making an LLM demo is easy: a couple of API calls and "seems to work". But production is quite different: real users, volume, cost, failures, security, quality you must hold for months. LLMOps (by analogy with DevOps/MLOps) is a set of practices for reliably operating AI systems in production. This course brings together what makes the difference between a "quick demo" and a system you can trust with users.
Demo vs production
Demo: works for you on a couple of examples, cost and failures do not matter.
Production: thousands of real requests, cost adds up, failures are seen by users, quality must be measured and held, security is critical.
What LLMOps covers
What is included in LLMOps and why is it needed?
LLMOps is the discipline of operating LLM systems in production, by analogy with DevOps (operating software) and MLOps (operating ML models). Why: an LLM prototype is easy to make, but turning it into a reliable system for real users is separate work. LLMOps covers everything needed for an AI system to work stably, predictably, safely and economically in production. The main areas (which the course covers): 1) Cost: LLM calls cost money (by tokens), and at volume this adds up to serious sums. You need to understand, track and optimize cost (model choice for the task, prompt length, caching). Uncontrolled cost is a common LLM-in-production problem. 2) Caching: many requests repeat; a cache saves answers and saves calls (money + speed). An important optimization technique. 3) Monitoring: you need to see what happens in the system — how many requests, errors, latencies, cost, answer quality. Without monitoring you are "blind" and will not notice problems. 4) Evaluation (evals): how to know whether the system answers well, and whether it got worse after changes? You need systematic ways to evaluate quality (not "seems to work"). This is one of the key and underrated parts. 5) Security: LLM systems have specific risks — prompt injections, data leaks, harmful output, abuse. You need protections. 6) Reliability: error handling, limits, retries, fallbacks (partly from the LLM-API course) — so the system does not fall from failures. 7) Deployment and versions: managing prompts, models, configuration as code; the ability to roll back, track changes. The key idea: LLM applications have THEIR OWN operation specifics, different from ordinary software: — Non-determinism (one input to different outputs), complicating testing. — Cost by tokens (unlike ordinary functions). — Quality is hard to measure (no single correct answer). — Specific security risks (injections). — Dependence on external models/APIs that change. LLMOps addresses these specifics. Practical meaning: if you build an AI system for real users, LLMOps is the difference between "worked on the demo" and "works reliably in production". The course brings together practices (many you met in the LLM-API, RAG, agents courses) into a whole operation discipline. You do not need to implement everything at once — start with the basics (cost, basic monitoring, error handling) and build up as the system grows. But understanding the full picture matters to build reliably from the start.
LLM systems have their own operation specifics
Why a separate discipline? LLM applications have their own specifics, different from ordinary software: non-determinism (one input to different outputs, hard to test), cost by tokens (unlike ordinary functions), quality is hard to measure (no single correct answer), specific risks (prompt injections), dependence on external models that change. LLMOps addresses exactly these specifics. Ordinary DevOps practices are not enough — additional, LLM-specific ones are needed.
Build up as it grows
You do not need to implement everything at once. Start with the basics (understanding cost, basic monitoring, error handling from the LLM-API course) and build up as the system grows (cache, systematic evaluations, security protections). But understanding the full picture matters from the start to build reliably. The course gives this picture: cost and cache, monitoring, evaluation, security — what turns a prototype into a system you can trust with real users.
Course rule: LLMOps is practices for reliably operating AI systems in production (cost, cache, monitoring, evaluation, security), addressing LLM specifics. Start with the basics, build up as it grows.
LLMOps is practices for reliably operating AI systems in production: cost, monitoring, evaluation, security.