Enterprise LLM integration has produced a set of patterns through failure and success that are now clear enough to document. Here is what we have learned from real implementations.
The prompt management problem is the first one teams hit. When prompts are strings scattered through the codebase, they become impossible to manage. You cannot version them, you cannot evaluate the impact of changes, you cannot A/B test variations. The solution is to treat prompts as first-class artifacts: versioned, stored in a prompt registry, evaluated against a test set before deployment. Teams that invested in prompt management early had significantly better outcomes.
The evaluation infrastructure problem comes second. You need to know whether a change to your prompts or models improved things. Without evaluation infrastructure, you are guessing. Building a golden dataset of example inputs and expected outputs, and testing every change against it, is not glamorous engineering but it is essential. The teams that skipped this step made decisions based on vibes and often deployed regressions.
The cost control problem hits harder than expected. Enterprise applications often have thousands of users and many AI interactions per session. A cost of fifty cents per conversation sounds trivial until you have ten thousand conversations per day. Tiered model routing (use a small cheap model for simple tasks, a large expensive model only when needed), aggressive caching, and token budgets per interaction are necessary engineering for sustainable economics.
The compliance and data governance problem is genuinely difficult. Most enterprises have constraints on what data can be sent to third-party APIs. This affects what LLM providers you can use, what data can appear in prompts, and what information can be included in responses. Designing LLM integrations around these constraints from the start is far easier than retrofitting them. Prompts should be auditable, data handling should be documented, and any PII in the system should be tracked.
The latency problem affects user experience in ways that matter. Language model inference takes time, especially for longer responses. Streaming responses to the user as they are generated dramatically improves perceived responsiveness. Every production LLM application should be streaming by default.
The reliability pattern that has worked best is treating LLM responses as potentially wrong and building verification into the flow. For decisions with consequences, a human review step or an automated verification step before action is taken. For information retrieval, a citation that can be checked. For code generation, automated testing. The philosophy of "trust but verify" reduces the risk of confident errors reaching users.