Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG

Building LLMs for Production is a comprehensive guide to building and deploying large language models (LLMs) for real-world applications. The book covers topics like model architecture, training data, fine-tuning, and evaluation. Bouchard and Peters provide practical examples and best practices for enhancing LLM abilities and reliability using techniques like prompting, fine-tuning, and RAG (Retrieval-Augmented Generation). They also discuss the challenges of deploying LLMs in production environments and share insights from their experience in natural language processing and machine learning. By equipping readers with the knowledge and skills to build and deploy LLMs for real-world use cases, the authors help organizations leverage the power of language models to drive innovation and create value.