Designing Large Language Model Applications

Chapter 13. Design Patterns & System Architecture

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Throughout this book, we have explored a variety of techniques to adapt LLMs to solve our tasks, including in-context learning, fine-tuning, retrieval augmented generation, and tool use. While these techniques can potentially be successful in satisfying the performance requirements of your use case, deploying an LLM-based application in production requires adherence to a variety of other criteria like cost, latency, and reliability. In order to achieve these goals, an LLM application needs a lot of software scaffolding and specialized components.

To this end, in this chapter we will discuss various techniques to compose a production-level LLM system that can power useful applications. We will explore how to leverage multi-LLM architectures to balance cost and performance. Finally, we will look into software frameworks like DSPy that integrate LLM application development into the ...