NVIDIA DLI–Building RAG Agents with LLMs Training Course

FAQ–Frequently asked questions about the NVIDIA DLI Building RAG Agents with LLMs Training Course What is a RAG agent?

A RAG (Retrieval-Augmented Generation) agent is an AI system that combines an LLM (Large Language Model) with a semantic search engine over your enterprise data (documents, knowledge bases, wikis). Instead of inventing answers, the LLM cites your internal sources, which drastically reduces hallucinations. MFE-IT teaches you how to design these agents with NVIDIA tools (NIM, NeMo, LangChain).

What is the difference between RAG and fine-tuning an LLM?

RAG injects dynamic context at each query (recent data, private documents) without modifying the model. Fine-tuning changes the LLM’s weights to specialise its behaviour (style, domain). RAG is faster, cheaper and adapts in real time to your data; fine-tuning is preferable to impose a very specific tone or structure. They are often combined.

Which NVIDIA tools are used in this training?

The training covers the official NVIDIA building blocks: NIM (NVIDIA Inference Microservices for serving LLMs), NeMo Retriever (GPU-optimised embeddings and reranking), LangChain and LlamaIndex for orchestration, and vector databases such as Milvus or FAISS. A cloud GPU environment is provided by NVIDIA during the session.

How long is the NVIDIA DLI RAG Agents training?

The MFE-IT training lasts 1 day (7 hours), in a fully tailored format with a maximum of 3 participants per session. It covers intensively: document ingestion, chunking, embeddings, vector indexing, retrieval, prompting and RAG agent orchestration. An NVIDIA DLI certificate is awarded on completion and 30 days of post-training support are included.

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Good to know

Training delivered in French, original content in English. Our sessions are guaranteed from a single registrant, with no risk of postponement (except in cases of force majeure). A prior interview takes place between the participant and a company representative to fully take into account the participant’s profile (level, needs, professional context, challenges, etc.). Evaluation: during the training, the trainer assesses participants’ learning progress through quizzes, role-plays and practical exercises. Participants receive a certificate of achievement at the end of the training. This training is part of our catalogue. Discover our other AI training courses to fully harness the potential of machine learning, LLMs and generative AI.

Objectives of the NVIDIA DLI Building RAG Agents with LLMs Training Course

• By the end of this training:
• Understand the RAG architecture and its benefits
• Build indexing and vector search pipelines
• Integrate external knowledge bases with LLMs
• Design autonomous agents with LangChain
• Deploy RAG solutions at scale

Prerequisites for the NVIDIA DLI Building RAG Agents with LLMs Training Course

Intermediate Python and basic knowledge of LLMs. The NVIDIA DLI Prompt Engineering training is recommended. Because every participant is unique, a personalised interview with our expert allows us to design a training course perfectly aligned with their goals, level and professional challenges.

Target audience of the NVIDIA DLI Building RAG Agents with LLMs Training Course

AI developers, solution architects, data engineers, and professionals wishing to master RAG on NVIDIA GPUs.