Explore comprehensive model adaptation strategies for large language models in this 25-minute conference talk from the AI Engineer Summit 2023. Learn about various adaptation methods ranging from prompt engineering and retrieval-augmented generation (RAGs) to fine-tuning techniques, with guidance on selecting the appropriate approach based on your dataset and specific problem requirements. Discover operational best practices for fine-tuning large language models and gain insights into evaluation methodologies tailored for specific business use cases. Examine a detailed comparative framework that analyzes the cost-benefit tradeoffs between fine-tuning approaches and knowledge base implementations for enhancing LLM performance on domain-specific tasks. The presentation concludes with practical guidance on when to choose fine-tuning versus knowledge bases based on your particular requirements and constraints.