Spots 
You built a Gen AI prototype over a weekend. The demo went beautifully. Your team was impressed, your manager asked when it could go live, and then it met real users. Suddenly there were hallucinated answers, slow responses, a cloud bill that made finance nervous, and a small prompt tweak that quietly broke three unrelated features.
Welcome to the gap between a prototype and a production system. Most Gen AI projects don't fail because the model is bad. They fail because the engineering around the model is missing. LLMOps is the discipline of closing that gap. This guide walks through the practices that matter most, in roughly the order you'll need them. Why LLM applications are different
If you've shipped traditional software, a lot of your instincts still apply. But LLM applications behave differently in ways that catch teams off guard.
Outputs are non-deterministic. The same input can produce different outputs. You can't write a simple assertion that says "the answer must equal X" for most tasks.
Behavior lives in text. Prompts, instructions, and retrieved documents shape what your application does. A change to a sentence can change behavior as much as a change to a function.
Quality is subjective and multidimensional. An answer can be accurate but rude, helpful but too long, or fluent but subtly wrong. There's no single test that says "correct."
Dependencies change under you. Model providers update models, deprecate versions, and adjust behavior. Your application can regress without you touching a line of code.
Costs scale with usage in unusual ways. Every request consumes tokens, and long prompts, large retrieved contexts, and chatty outputs add up fast.
LLMOps is simply the set of practices that make these properties manageable: versioning, evaluation, monitoring, cost control, security, and feedback loops. 1. Treat prompts like code Prompts are application logic, so give them the same discipline as code.
Version control. Store prompts in your repository, not as strings scattered through the codebase or pasted into a dashboard where nobody tracks changes.
You built a Gen AI prototype over a weekend.