“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Adding New Knowledge to LLMs
Explore how to add domain knowledge to large language models through data preparation, fine-tuning, alignment, compression, decoding, and evaluation techniques.
Track: NVIDIA DLI Certificate of CompetencyNVIDIAIntermediate
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration8 hours
FormatVirtual, On-site, or Hybrid
LevelIntermediate
Certification TrackNVIDIA DLI Certificate of Competency
SubjectLLM Customization
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Adding New Knowledge to LLMs as an MCT-led NVIDIA program for AI / LLM Engineer. Typical duration is 8 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
Practitioners who need to adapt language models to specialized business or technical knowledge.
- AI / LLM Engineer
- LLM Customization
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
NVIDIA Deep Learning Institute-aligned workshop for adding specialized knowledge to large language models.
Prerequisites
- Experience developing an LLM application
- Working knowledge of RAG and fine-tuning concepts
- Python and deep learning familiarity
Program Outcomes
Capabilities your teams will gain
- Differentiate RAG, fine-tuning, and alignment
- Create diverse synthetic datasets
- Apply parameter-efficient fine-tuning and compression techniques
- Evaluate customized model output
Enterprise Delivery
Official learning & delivery options
Review the vendor learning resource, then book a consultation or request a private cohort for your team.
Curriculum
Curriculum roadmap
- RAG, fine-tuning, and alignment
- Synthetic data strategies
- Parameter-efficient fine-tuning
- Pruning and distillation
- Decoding and LLM evaluation
1Module 1
Choose a knowledge-adaptation strategy
+
Module 1
Choose a knowledge-adaptation strategy
Compare retrieval, fine-tuning, and alignment for domain-specific requirements.
- Compare retrieval
- and alignment for domain-specific requirements
2Module 2
Customize efficiently
+
Module 2
Customize efficiently
Use synthetic data, parameter-efficient methods, pruning, and distillation.
- Use synthetic data
- parameter-efficient methods
- and distillation
3Module 3
Evaluate model behavior
+
Module 3
Evaluate model behavior
Assess output with task metrics, semantic similarity, and model-based evaluation.
- Assess output with task metrics
- semantic similarity
- and model-based evaluation
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Intermediate practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds practical judgment for selecting and applying customization techniques when retrieval alone is not enough.
- Align examples to an enterprise knowledge domain
- Add model-governance and evaluation criteria
- Extend into a supervised customization pilot
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
