LLM Design Patterns

The integration of LLMs into business operations demands a robust framework of best practices and design patterns to ensure efficient and effective deployment. Generalizing these best practices and creating generic templates can streamline the adoption process, allowing businesses to leverage LLMs more seamlessly across various applications. This section will define key design patterns including dynamic batching for inference, model compression techniques, and evaluation and monitoring strategies, ensuring a balance between utility and complexity, and embedding business metrics into the process. The in-depth details and implementation of these patterns will be addressed in subsequent chapters. For now, it will be enough to have a general idea of what each pattern entails.

Dynamic Batching for Inference

Dynamic batching is a critical design pattern for optimizing inference in LLMs. By grouping multiple requests into batches, dynamic batching improves GPU utilization...