NVIDIA-Certified Associate: Generative AI LLMs Study Roadmap

Review neural networks, transformers, attention, embeddings, tokenization, context windows, autoregressive generation, and hallucinations. Your checkpoint is being able to explain why a model may produce fluent but wrong output and why adding context at inference time is different from adapting model weights.

Study system prompts, role framing, task decomposition, examples, output schemas, and prompt testing. Compare zero-shot and few-shot approaches. Many candidates assume longer prompts are always better; in practice, prompt structure, relevant context, and evaluation matter more than raw prompt length.

Move next into RAG: document ingestion, chunking, embeddings, vector databases, semantic search, metadata filtering, reranking, and grounded generation. A good checkpoint is knowing when RAG is better than fine-tuning: use retrieval when the answer depends on private, current, or source-specific knowledge.

Review how LLM applications call APIs, validate outputs, manage errors, and serve models. Then study NIM, Triton Inference Server, TensorRT-LLM, and NeMo at a conceptual level. Focus on what each solves: serving, optimization, model development, customization, or application integration.

Study evaluation datasets, benchmark choice, human review, automated metrics, RAG evaluation, red-team testing, and regression checks. A model demo can look impressive while failing edge cases. Good AI engineering requires test sets that reflect the user tasks, policy constraints, and failure modes of the application.

Review alignment, guardrails, responsible AI, logging, privacy, bias, unsafe output handling, accountability records, and governance review. NeMo Guardrails is relevant to policy-based conversational controls, but safe deployment also depends on evaluation, monitoring, access control, and operational ownership.