“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
AI and Generative AI Foundations
Understand where generative AI fits and assess a bounded business use case. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSFoundation
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration16 hours
FormatPrivate cohort
LevelFoundation
Course CodeF01
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers AI and Generative AI Foundations as an MCT-led VNode ITeS program for Business professionals, Technical practitioners. Typical duration is 16 hours (Private cohort). Delivery stays scoped to how your teams will actually use the platform after the room empties. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Business professionals, Technical practitioners
- Tailored Team Delivery
Overview
Executive overview
Understand where generative AI fits and assess a bounded business use case.
Environment and licensing
Spreadsheet or document editor Synthetic scenario pack Approved AI interface only where used; paper-based comparison available Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- General business or technical literacy
Program Outcomes
Capabilities your teams will gain
- Classify candidate tasks using an explicit AI-versus-automation rationale
- Define baseline measures and observable pilot acceptance
- Identify sensitive information and human approval boundaries
- Present an owned use-case brief with assumptions and exclusions
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
Proposed instructor-led duration; delivery subject to environment and lab validation.
- AI capabilities and limits
- Prompts, context and grounding
- Quality, risk and human oversight
- Applied AI use-case workshop
1Module 1 · 4 hours
AI capabilities and limits
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Module 1 · 4 hours
AI capabilities and limits
Compare AI and deterministic automation for three business tasks.
Prediction versus generation
- Compare classification, forecasting and generation
- Match tasks to rules-based automation or AI
LLMs and tokens
- Explain tokens, context and inference without assuming database-like accuracy
- Distinguish model knowledge from supplied enterprise context
Probabilistic output
- Identify variable answers and plausible fabrication
- Recognise that fluent output is not verified evidence
Appropriate and inappropriate use cases
- Select low-risk bounded use cases
- Reject cases lacking usable data or accountable ownership
Concept and design 2h · applied exercise 1.5h · review 0.5h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: AI capabilities and limits
Compare AI and deterministic automation for three business tasks.
Expected output. Use-case suitability matrix
Acceptance. Justify three task classifications using business requirements rather than tool preference.
2Module 2 · 4 hours
Prompts, context and grounding
+
Module 2 · 4 hours
Prompts, context and grounding
Improve a prompt and compare grounded and ungrounded answers using supplied documents.
Instructions and examples
- Separate system instructions, user requests and examples
- Specify audience, format and response constraints
Context limits
- Identify context-window limits and irrelevant context
- Compare concise evidence with unfiltered document dumps
Structured responses
- Request machine-readable fields and validate the result
- Handle missing information without inventing values
Retrieval versus model training
- Compare retrieval grounding with fine-tuning conceptually
- Check citations against supplied source passages
Concept and design 2h · applied exercise 1.5h · review 0.5h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Prompts, context and grounding
Improve a prompt and compare grounded and ungrounded answers using supplied documents.
Expected output. Prompt comparison and grounding record
Acceptance. Show a prompt revision improves the chosen criteria without hiding unsupported answers.
3Module 3 · 4 hours
Quality, risk and human oversight
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Module 3 · 4 hours
Quality, risk and human oversight
Score sample responses and identify information and action boundaries.
Hallucinations
- Identify factual errors and unsupported claims
- Separate uncertain answers from acceptable refusals
Sensitive information
- Classify information before submitting it to an AI tool
- Recognise personal data, confidential content and secrets
Prompt injection concepts
- Explain how untrusted content can alter agent behaviour
- Distinguish instructions from quoted external evidence
Evaluation criteria and human review
- Define when a human approves an action
- Score answers using a consistent checklist
Concept and design 2h · applied exercise 1.5h · review 0.5h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Quality, risk and human oversight
Score sample responses and identify information and action boundaries.
Expected output. Response scorecard and risk notes
Acceptance. Detect planted unsupported claims and explain appropriate escalation.
4Module 4 · 4 hours
Applied AI use-case workshop
+
Module 4 · 4 hours
Applied AI use-case workshop
Present a small AI use case with a non-AI alternative and testable acceptance criteria.
Business outcome
- Define the operational problem and target users
- State a measurable current-state baseline
Users and workflow
- Map inputs, decision points and possible outputs
- Compare AI-assisted and deterministic workflow options
Baseline and success measures
- Choose success metrics and a bounded pilot
- Record assumptions and data-access dependencies
Acceptance and ownership
- Assign business and technical owners
- Present acceptance criteria and stop conditions
Concept and design 1h · applied exercise 2h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Applied AI use-case workshop
Present a small AI use case with a non-AI alternative and testable acceptance criteria.
Expected output. Assessed AI use-case brief
Acceptance. Submit a use case with measurable acceptance, an owner and a justified non-AI alternative.
Capstone
Present a small AI use case with a non-AI alternative and testable acceptance criteria. Assessed AI use-case brief Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Classify candidate tasks using an explicit AI-versus-automation rationale 30% Define baseline measures and observable pilot acceptance 25% Identify sensitive information and human approval boundaries 25% Present an owned use-case brief with assumptions and exclusions 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed AI use-case brief
- Evidence of relevant access, quality and risk controls
- Demonstration and limitations record
Scope boundaries
Step-by-step lab instructions; Runnable solution code; Packaged source datasets; Executed environment validation
Delivery options
Count teaching hours, not calendar days. Exclude breaks. Split into 4-hour or 8-hour teaching sessions; schedule complex project work in contiguous blocks.
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Private cohort delivery
- Foundation practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Understand where generative AI fits and assess a bounded business use case.
- Private cohort scheduling
- Equivalent-experience entry review
Resources
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
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
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
