With retrieval mechanisms for large-scale RAG systems established, this chapter centers on the Large Language Model (LLM) component. The efficiency and effectiveness of the LLM significantly influence the overall performance, cost, and quality of responses in distributed RAG.

In this chapter, you will learn practical methods for enhancing LLM operations within these large systems. We will cover:

• Efficient LLM Serving: Architectures designed for high-throughput LLM deployment in RAG, including tools like vLLM and Text Generation Inference (TGI).
• Model Adaptation: Using Parameter-Efficient Fine-Tuning (PEFT) techniques to customize LLMs for specific domains and RAG tasks.
• Deployment Optimization: Applying methods such as quantization and pruning to reduce LLM size and inference costs, making them more suitable for production.
• Long Context Management: Strategies for LLMs to effectively process and utilize the large volumes of information retrieved by the RAG system.
• Mitigating Hallucinations: Techniques to reduce the likelihood of LLMs generating factually incorrect or irrelevant information, especially when dealing with extensive context at scale.
• Multi-LLM Architectures: Designing RAG systems that incorporate multiple LLMs and intelligent routing logic to optimize response generation.

The chapter also includes a hands-on section focused on fine-tuning an LLM, providing an opportunity to apply these optimization techniques to improve performance on a specific RAG task.