“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Prompt Engineering with LLaMA-2
Covers how to interact with and prompt-engineer Llama 2 models for document analysis, text generation, and assistant use cases.
Duration
2 hours
Level
Beginner
Format
Virtual, On-site, or Hybrid
Language
English
NVIDIA
Prompt EngineeringPrompt Engineering with LLaMA-2
NVIDIA NeMo
On this page
Ideal for
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Prompt Engineering with LLaMA-2 as an MCT-led NVIDIA program for AI / LLM Engineer. Typical duration is 2 hours (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for teams improving enterprise LLM usage through prompt design.
Overview
Executive overview
Official NVIDIA self-paced course on prompt engineering with Llama 2.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in prompt engineering scenarios
Strengthen capability in nvidia nemo scenarios
Strengthen capability in generative ai / llm scenarios
Curriculum
Curriculum roadmap
Prompt Engineering
NVIDIA NeMo
Generative AI / LLM
1Module 1
Prompt Engineering
+
Module 1
Prompt Engineering
Cover prompt engineering skills and implementation practices aligned to Prompt Engineering with LLaMA-2.
- Cover prompt engineering skills
- implementation practices aligned to Prompt Engineering with LLaMA-2
2Module 2
NVIDIA NeMo
+
Module 2
NVIDIA NeMo
Cover nvidia nemo skills and implementation practices aligned to Prompt Engineering with LLaMA-2.
- Cover nvidia nemo skills
- implementation practices aligned to Prompt Engineering with LLaMA-2
3Module 3
Generative AI / LLM
+
Module 3
Generative AI / LLM
Cover generative ai / llm skills and implementation practices aligned to Prompt Engineering with LLaMA-2.
- Cover generative ai / llm skills
- implementation practices aligned to Prompt Engineering with LLaMA-2
Delivery Models
Delivery models
Engagement Fit
Engagement fit
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps teams improve LLM usefulness quickly through better prompting and model interaction practices.
- •Use your enterprise assistant scenario
- •Add evaluation and guardrail patterns
Credentials
Certification & official source
- •NVIDIA DLI Certificate of Competency
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
