Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical Blog

This post details nine techniques for customizing AI agents to excel at specialized tasks beyond general-purpose foundation models. It covers prompt engineering, retrieval-augmented generation (RAG), agent tool and skill injection, supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), Direct Preference Optimization (DPO), and reinforcement learning with verifiable rewards (RLVR). Each technique is explained with its mechanism, use cases, and limitations, providing guidance on selecting the appropriate method based on the need for better information, instructions, or more reliable behavior.