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Enterprise Program Brief
AI Evaluation and Quality Engineering (O01)
Build a repeatable quality gate for a supplied AI application. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSPractitioner / specialist
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration40 hours
FormatPrivate cohort
LevelPractitioner / specialist
Course CodeO01
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers AI Evaluation and Quality Engineering (O01) as an MCT-led VNode ITeS program for QA engineers, AI engineers, Technical reviewers. Typical duration is 40 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
QA engineers, AI engineers, Technical reviewers
- Tailored Team Delivery
Overview
Executive overview
Build a repeatable quality gate for a supplied AI application.
Environment and licensing
Isolated Azure subscription/resource group Scoped deployment rights and resource-provider access Approved region, quota and optional model deployment Cost budget and cleanup plan Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Can run and modify a working AI application
- Python and API competence
- Applied AI Engineering with Microsoft Foundry, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Implement a versioned evaluation dataset and scorer suite
- Calibrate automated results against references and human review
- Block critical unsafe-action and access regressions
- Defend an evidence-based release or hold recommendation
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.
- Evaluation strategy and task contracts
- Datasets and reference evidence
- Scorers and human calibration
- Agent and retrieval diagnostics
- Quality engineering capstone
1Module 1 · 8 hours
Evaluation strategy and task contracts
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Module 1 · 8 hours
Evaluation strategy and task contracts
Define quality criteria and failure severity for an enterprise assistant.
Task success
- Define the unit of evaluation and task boundary
- Separate component scores from workflow success
Failure taxonomy
- Create a failure taxonomy with severity
- Distinguish correctness, relevance, safety and access
Quality versus safety
- Choose release thresholds before comparing versions
- Avoid single-score claims without error analysis
Release thresholds
- Define evaluation ownership and review cadence
- Record expected limits of automated scoring
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Evaluation strategy and task contracts
Define quality criteria and failure severity for an enterprise assistant.
Expected output. Evaluation strategy and acceptance thresholds
Acceptance. The evaluation strategy states what passes, what blocks release and who decides.
2Module 2 · 8 hours
Datasets and reference evidence
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Module 2 · 8 hours
Datasets and reference evidence
Create normal, ambiguous, denied-access and attack cases with reference evidence.
Representative cases
- Sample real task patterns using synthetic data
- Include volume and risk-relevant cases
Ground truth
- Write reference answers and supporting passages
- Record where multiple outcomes are valid
Adversarial cases
- Include unsupported, ambiguous and adversarial inputs
- Avoid leaking evaluation answers into prompts
Dataset versioning
- Version datasets and track source changes
- Separate development and held-out examples
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Datasets and reference evidence
Create normal, ambiguous, denied-access and attack cases with reference evidence.
Expected output. Versioned evaluation dataset
Acceptance. The dataset supports repeatable comparisons and documents coverage gaps.
3Module 3 · 8 hours
Scorers and human calibration
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Module 3 · 8 hours
Scorers and human calibration
Implement checks and calibrate automated scoring against human labels.
Deterministic tests
- Use schema, exact-value and invariant checks
- Prefer deterministic scoring where ground truth permits
LLM judges
- Design judge instructions and evidence inputs
- Control model and scoring configuration
Human review
- Compare human and automated labels
- Investigate disagreement rather than averaging it away
Bias and judge disagreement
- Calibrate acceptance thresholds and severity
- Document judge limitations and repeatability
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Scorers and human calibration
Implement checks and calibrate automated scoring against human labels.
Expected output. Calibrated scorer suite
Acceptance. Scorers are justified and disagreements with human review are recorded.
4Module 4 · 8 hours
Agent and retrieval diagnostics
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Module 4 · 8 hours
Agent and retrieval diagnostics
Find root causes and compare baseline and changed application versions.
Tool correctness
- Measure tool selection, arguments and result handling
- Check actions against allowed workflow transitions
Retrieval quality
- Evaluate retrieval relevance and source permission
- Distinguish retrieval failure from generation failure
Trace analysis
- Use traces to locate failed stages
- Relate errors to input and configuration versions
Cost and latency
- Compare quality with latency and usage
- Report regressions by severity and affected task
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Agent and retrieval diagnostics
Find root causes and compare baseline and changed application versions.
Expected output. Diagnostic and regression report
Acceptance. The diagnostic report identifies actionable failure causes and fair version comparisons.
5Module 5 · 8 hours
Quality engineering capstone
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Module 5 · 8 hours
Quality engineering capstone
Run the evaluation gate and defend a release or hold recommendation.
CI integration
- Automate the agreed evaluation on a versioned candidate
- Preserve results as release evidence
Threshold enforcement
- Block the release on defined critical failures
- Allow only documented exceptions
Exceptions
- Summarise strengths, failures and uncertainty
- Avoid claiming untested general reliability
Release review
- Present release, hold or limited-pilot recommendation
- Define required retest after fixes
Concept and design 2h · applied exercise 4h · review 2h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Quality engineering capstone
Run the evaluation gate and defend a release or hold recommendation.
Expected output. Assessed AI quality gate
Acceptance. The quality gate produces a reproducible and justified release decision.
Capstone
Run the evaluation gate and defend a release or hold recommendation. Assessed AI quality gate Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Implement a versioned evaluation dataset and scorer suite 30% Calibrate automated results against references and human review 25% Block critical unsafe-action and access regressions 25% Defend an evidence-based release or hold recommendation 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed AI quality gate
- 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
- Practitioner / specialist practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Build a repeatable quality gate for a supplied AI application.
- 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
