Learning path·Intermediate · 40–60 hours

Become an AI Engineer with hands-on, GPU-backed labs

From transformers to production-grade RAG agents. Hands-on, GPU-backed.

115 interactive lessons49 coding challenges6 projects, auto-graded26 GPU labs
8
Modules
115
Interactive lessons
49
Coding challenges
26
Hands-on GPU labs
6
Build projects
3
Cert checkpoints
What you'll build
from text to agent
Fine-tune · LoRA adapter
W · frozen
+
×
A · B · trained
Fine-tune it on your data. LoRA adapts a frozen model cheaply.

What you'll be able to build

Eight capabilities, previewed with the actual animations from each module.

Self-Attention
The same token, rewritten by context
Sentence ①
I
sat
by
the
river
BANK
BANK · meaning vector
water
nature
place
money
finance
building

Build a transformer from scratch. Attention, multi-head, residuals, and the full block, trained on real data.

Open this module

About this path

An AI Engineer is the role that ships LLM-powered features to production: choosing the right model, fine-tuning when off-the-shelf isn't enough, building retrieval and tool-use loops, and running it all on GPUs that don't time out. This path teaches the role through real labs. Every concept ends in code that runs against a real GPU.

Skills you'll put on a resume

  • Implement a decoder-only transformer end-to-end and train it on real data
  • Fine-tune open-weight LLMs with LoRA and QLoRA on a single GPU
  • Build a production RAG pipeline with hybrid search, reranking, and grounded generation
  • Serve LLMs at production throughput with vLLM (PagedAttention, continuous batching, prefix caching)
  • Evaluate models with perplexity, LLM-as-judge, and preference data
  • Build production agents with ReAct, MCP tool servers, multi-agent orchestration, and memory
  • Pass the NVIDIA NCA-GENL, NCP-GENL, and NCP-AAI certifications

For

Software engineers comfortable with Python and basic ML who want to move into LLM/AI engineering work

Prerequisites

  • Comfortable Python (functions, classes, package management)
  • Familiarity with PyTorch or another ML framework
  • Basic ML literacy (training/eval, gradient descent, overfitting)

Guides & articles

Deep-dive reading that pairs with this course

NCA-GENL Complete Guide 2026 — NVIDIA Generative AI LLM Associate

Master the NVIDIA Certified Associate - Generative AI with LLMs (NCA-GENL) certification with this comprehensive 2026 guide. Get exam details, study strategies, career insights, and actionable preparation steps for beginners.

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NCA-GENL Cheat Sheet 2026: Quick Reference for Exam Day

Essential formulas, code snippets, and quick reference for NVIDIA Certified Associate - Generative AI with LLMs (NCA-GENL) exam. One-page study guide with transformer architecture, prompt engineering, LoRA, RAG, and evaluation metrics.

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How to Pass NCA-GENL on Your First Attempt (2026 Tips)

The complete guide to passing NVIDIA Certified Associate - Generative AI with LLMs (NCA-GENL) on your first try. Includes study plan, domain breakdown, common mistakes, and exam day strategies for beginners.

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NCA-GENL Exam Domains 2026: Weights, Topics & Study Strategy

Master all 4 NVIDIA NCA-GENL exam domains with this detailed breakdown. Learn domain weights, key topics, and proven strategies to pass the Generative AI LLM Associate certification.

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NCA-GENL 4-Week Study Plan: Week-by-Week Preparation Guide

Pass the NVIDIA NCA-GENL certification with this structured 4-week study plan. Covers all 4 domains with daily tasks, recommended resources, and practice exam milestones.

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NCP-GENL Complete Guide 2026 — NVIDIA Generative AI LLM Professional

Master the NVIDIA Certified Professional - Generative AI LLMs (NCP-GENL) certification with this comprehensive guide. Get exam details, study strategies, salary insights, and actionable preparation steps.

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How to Pass NCP-GENL on Your First Attempt (2026 Tips)

The complete guide to passing NVIDIA Certified Professional - Generative AI LLMs (NCP-GENL) on your first try. Includes study plan, domain breakdown, common mistakes, hands-on projects, and exam day strategies.

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NCP-GENL Cheat Sheet 2026: Quick Reference for Exam Day

Essential formulas, optimization techniques, and quick reference for NVIDIA Certified Professional - Generative AI LLMs (NCP-GENL) exam. Covers TensorRT-LLM, quantization, distributed training, parallelism strategies, and production deployment.

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NCP-GENL Exam Domains 2026: Weights, Topics & Study Strategy

Master all 5 NVIDIA NCP-GENL exam domains with this detailed breakdown. Learn domain weights, key topics, tested skills, and proven strategies to pass the Generative AI LLM Professional certification.

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NCP-GENL 8-Week Study Plan: Week-by-Week Preparation Guide

Master the NVIDIA NCP-GENL certification with this structured 8-week study plan. Covers all 5 domains with daily tasks, hands-on labs, and practice exam milestones.

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NCP-GENL vs NCA-GENL: Professional vs Associate — Which NVIDIA LLM Cert?

NCP-GENL vs NCA-GENL compared side-by-side. Exam cost, difficulty, domains, career impact, and whether you can skip the Associate to go straight to Professional.

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NCP-GENL Model Optimization: Quantization, Pruning & TensorRT Guide

Master the NCP-GENL Model Optimization domain (17%). Covers quantization, pruning, distillation, TensorRT-LLM, and memory optimization for production LLMs.

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GPU Acceleration for NCP-GENL: Distributed Training & Parallelism Strategies

Master the NCP-GENL GPU Acceleration domain (14%). Covers data, model, tensor, and pipeline parallelism, DeepSpeed, Megatron-LM, and NVIDIA Nsight.

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NCP-GENL Fine-Tuning Guide: LoRA, QLoRA & PEFT for Production LLMs

Master the NCP-GENL Fine-Tuning domain (13%). Covers LoRA, QLoRA, PEFT, adapter methods, NeMo Framework, and catastrophic forgetting prevention.

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Best NCP-GENL Practice Tests 2026: Where to Prepare for the Exam

Compare the best NCP-GENL practice test platforms in 2026 — pricing, question quality, domain coverage, and which gives you the best shot at passing.

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NCP-AAI Complete Guide 2026 — NVIDIA Agentic AI Certification

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What is NCP-AAI? NVIDIA Agentic AI Certification Explained

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NCP-AAI Exam Format 2026: Questions, Duration & What to Expect

Complete breakdown of the NVIDIA NCP-AAI exam format and structure. 70 questions, 120 minutes, 10 domains, 70% passing score. Know exactly what to expect.

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NCP-AAI Prerequisites: What You Need Before the Exam

Essential prerequisites and skills for the NVIDIA NCP-AAI certification. Python, AI/ML experience, agent frameworks, and recommended background for success.

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NCP-AAI vs AWS vs Google AI Certs: Which Should You Get?

Compare NVIDIA NCP-AAI with AWS AI, Google Cloud ML, Azure AI, and other certifications. Salary data, difficulty, career impact, and which to choose in 2026.

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How Long to Prepare for NCP-AAI? Realistic Study Timeline

How long does NCP-AAI certification preparation take? Realistic timelines from 4-12 weeks based on your experience level, with daily study recommendations.

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NCP-AAI Cost & ROI: Is the $200 Exam Worth It in 2026?

Complete NCP-AAI certification cost and ROI analysis. Exam fees, study materials, time investment, and salary impact. Is the NVIDIA agentic AI certification worth it?

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AI Agent Architecture Patterns: ReAct, Plan-Execute & More

Master agent architecture design patterns for the NVIDIA NCP-AAI exam. Covers ReAct, Plan-and-Execute, Reflexion, and more with practical examples and exam strategies.

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Multi-Agent Coordination: Orchestration Patterns for NCP-AAI

Master multi-agent collaboration and coordination patterns for the NVIDIA NCP-AAI certification. Covers orchestrator-worker, sequential, group chat, hierarchical patterns, A2A protocol, and NVIDIA Agent Blueprints.

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NCP-AAI Practice Tests: Why You Need Them to Pass

Discover why NCP-AAI practice tests are essential for passing the NVIDIA agentic AI certification. Learn how to use practice exams effectively for first-attempt success.

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RAG for AI Agents: Retrieval-Augmented Generation NCP-AAI Guide

Complete guide to RAG systems and knowledge integration for the NVIDIA NCP-AAI exam. Covers 5-stage pipeline design, chunking strategies, embedding models, vector databases, reranking, agentic RAG patterns, and retrieval optimization with NVIDIA NIM and NeMo Retriever.

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NVIDIA NIM Deployment Guide: Docker, K8s & Cloud for AI Agents

Learn NVIDIA NIM deployment strategies for the NCP-AAI exam. Covers Docker quickstart, Kubernetes NIM Operator, cloud marketplace deployments, TensorRT optimization, scaling, LangChain integration, and production configuration.

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LangChain vs LlamaIndex for NCP-AAI: Which Framework in 2026?

LangChain vs LlamaIndex comparison for the NVIDIA NCP-AAI exam. Covers when to use each framework, strengths, trade-offs, and agent development patterns.

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Tool Calling in AI Agents: NCP-AAI Function Integration Guide

Master tool use and function calling for the NVIDIA NCP-AAI exam. Covers parameter validation, error handling, tool selection strategies, and implementation patterns.

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AI Agent Memory Systems: Complete NCP-AAI Guide 2026

Essential memory management patterns for AI agents on the NVIDIA NCP-AAI exam. Covers short-term, long-term, episodic, and semantic memory architectures.

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Prompt Engineering for AI Agents: NCP-AAI Best Practices

Prompt engineering best practices for the NVIDIA NCP-AAI exam. Covers system prompts, few-shot for agents, chain-of-thought, and tool-calling prompt design.

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Agent Planning: ReAct vs CoT vs Tree of Thoughts (NCP-AAI)

Master agent planning strategies for the NVIDIA NCP-AAI exam. Covers ReAct, Chain-of-Thought, Tree of Thoughts, HTN planning, MCTS, A*, forward/backward planning, and when to use each approach.

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Building Production AI Agents: NCP-AAI Deployment Guide 2026

Learn how to build production-ready AI agents for the NVIDIA NCP-AAI exam. Covers deployment patterns, monitoring, error handling, and scaling best practices.

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ChromaDB vs Pinecone vs Weaviate: Vector DBs for AI Agents

Compare vector databases for agentic AI on the NVIDIA NCP-AAI exam. ChromaDB vs Pinecone vs Weaviate — features, performance, and when to use each.

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LLM Fine-Tuning for AI Agents: LoRA, QLoRA & NeMo Guide 2026

Complete guide to LLM fine-tuning with NVIDIA NeMo, LoRA, QLoRA, and PEFT for the NCP-AAI exam. Covers method selection, LoRA rank guidance, QLoRA memory savings, RLHF for agents, domain-specific fine-tuning, RAG vs fine-tuning decision matrix, and production deployment with NIM.

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NCP-AAI 30-Day Study Plan: Week-by-Week Preparation Guide

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AI Agent Evaluation Metrics: CLASSic Framework & Benchmarks

Essential agent performance metrics for the NVIDIA NCP-AAI exam. Covers the CLASSic framework, task completion, latency, cost, accuracy, benchmarks like AgentBench, GAIA, SWE-bench, WebArena, and testing strategies for production agents.

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10 NCP-AAI Exam Mistakes That Cost You a Pass (And How to Avoid Them)

**Target Audience:** NCP-AAI exam candidates | **Reading Time:** 8 minutes | **Last Updated:** December 2025

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NVIDIA AI Enterprise for Agents: Platform Integration Guide

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Agent Reasoning & Cognitive Architectures for NCP-AAI 2026

Master agent reasoning techniques and cognitive architectures for the NVIDIA NCP-AAI exam. Covers reasoning frameworks, decision-making, and implementation.

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AI Safety Guardrails: NeMo Guardrails for NCP-AAI Agents

Master safety guardrails for agentic AI systems on the NVIDIA NCP-AAI exam. Covers NeMo Guardrails, content filtering, action constraints, and sandboxing.

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AI Agent Ethics & Compliance: GDPR, AI Act & NCP-AAI Guide

Ethics and compliance guide for the NVIDIA NCP-AAI exam. Covers GDPR, EU AI Act, bias mitigation, responsible AI governance, and compliance frameworks.

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NVIDIA Triton for AI Agents: Inference Server Deployment Guide

Guide to NVIDIA Triton Inference Server for agentic AI on the NCP-AAI exam. Covers configuration, dynamic batching, multi-model serving, and optimization.

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AI Agent Monitoring: Observability & Alerting Best Practices

Agent observability and monitoring best practices for the NVIDIA NCP-AAI exam. Covers distributed tracing, alerting, metrics, and production monitoring.

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Error Handling in AI Agents: Circuit Breakers, Retry & Recovery

Master error handling and resilience patterns for agentic AI systems on the NVIDIA NCP-AAI exam. Covers circuit breaker, retry with exponential backoff, fallback chains, graceful degradation, and recovery strategies for production agents.

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Testing AI Agents: Unit, Integration & Evaluation Strategies

Testing strategies for agentic AI applications on the NVIDIA NCP-AAI exam. Covers unit testing, integration testing, end-to-end, and adversarial testing.

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NVIDIA Riva for AI Agents: Speech Integration NCP-AAI Guide

Guide to NVIDIA Riva Speech AI integration with agentic systems for the NCP-AAI exam. Covers speech recognition, synthesis, and voice-enabled agent design.

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Agent State Management: Persistence Patterns for NCP-AAI

Agent state management and persistence guide for the NVIDIA NCP-AAI exam. Covers stateful vs stateless design, persistence backends, and session management.

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LangGraph vs AutoGen: Best Multi-Agent Framework for NCP-AAI

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NVIDIA NIM + LangChain: Production Integration for AI Agents

Guide to integrating NVIDIA NIM with LangChain for production agents on the NCP-AAI exam. Covers setup, optimization, and deployment patterns.

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Hugging Face for AI Agents: Transformers & NCP-AAI Guide

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NCP-AAI Salary Guide 2026: How Much Can You Earn?

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How to Pass NCP-AAI on Your First Attempt (2026 Tips)

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NCP-AAI Cheat Sheet 2026: Quick Reference for Exam Day

Essential patterns, architectures, and quick reference for NVIDIA Certified Professional - Agentic AI (NCP-AAI) exam. Covers all 10 domains: agent design, RAG, memory, NVIDIA platform, deployment, safety, and evaluation.

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NCP-AAI vs NCP-GENL: Which NVIDIA AI Cert Should You Get First?

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Best NCP-AAI Practice Tests 2026: Preporato vs Udemy vs Others

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Best NVIDIA Certification Practice Exams 2026 (Compared & Ranked)

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