Building RAG Agents With LLMs

Why it matters

Prompt structure and context assembly are easiest to judge in an agent that retrieves before it answers. This course is NVIDIA’s own structured route to building one.

What to practise

The course is NVIDIA Deep Learning Institute training in building retrieval-augmented generation (RAG) agents with large language models; its outline is on the course page. As you work through it, note where each part of a model request comes from — the fixed instructions, the retrieved passages, the conversation so far — and what checks a passage passes before it reaches the prompt.

How it fits

Hold each prompt you write against the layout in 2.1.1 and each answer format against 2.1.2. Compare the way the course assembles retrieved context with 2.1.3, which builds every request from validated state and selected passages, and keep 2.1.4 in mind whenever a model or prompt changes under a working pipeline.