Mastering Agentic Techniques: AI Agent Customization | NVIDIA Technical Blog

What this post added

This post details nine techniques for customizing AI agents to enhance their performance on specialized tasks. It covers prompt engineering, retrieval-augmented generation (RAG), agent tool and skill injection, supervised fine-tuning (SFT), and parameter-efficient fine-tuning (PEFT). It also introduces advanced methods like Direct Preference Optimization (DPO) and reinforcement learning with verifiable rewards (RLVR), often paired with Group Relative Policy Optimization (GRPO), for nuanced alignment and improved reasoning by leveraging preference signals or objective correctness criteria. The post provides criteria for selecting the right techniques based on use case needs for better information, instructions, or more reliable behavior.