# **10 LLM API Patterns Every Developer Should Know**
Mastering Large Language Model (LLM) APIs isn’t just about slapping together a few function calls and hitting "submit." Behind every smooth AI interaction lies a carefully crafted architecture—one that balances simplicity with scalability, security with usability. From optimizing cost per request to handling rate limits like a pro, developers who understand these patterns avoid common pitfalls and build systems that actually work in production.
The real magic happens when you start thinking beyond the basic `generate_text` endpoint: how do you handle batch processing? What’s the best way to paginate long responses? And why does every LLM have different quirks for tokenization, safety filters, or model-specific tuning? This guide breaks down 10 practical API patterns that will make your LLM integrations faster, cheaper, and more reliable—without needing a PhD in AI.
## **1. Batch Processing vs. Sequential Calls: When to Optimize for Bulk**
LLM APIs often resist bulk operations because they’re designed for single-token-at-a-time generation (or at least per-message chunks). But if you’re processing documents, generating summaries, or even debugging code snippets, batching requests can **cut costs by 50%+** and speed up workflows by reducing latency spikes.
