Certified Generative AI Specialist (CGAIS) ™
Validate the ability to apply foundational Generative AI concepts, prompt engineering, LLM production practices, responsible/ethical principles, security & compliance controls, and real-world solution design to practical scenarios.
Self-paced Course
Learn with labs & projects at your own pace.
Practice Exam
Timed questions with instant feedback & review.
Build capability that holds up in production, review, and audit.
Generative AI is moving from experimentation to regulated, high-visibility deployments. This certification emphasizes practical decision-making: interaction design, deployment trade-offs, evaluation, governance, and secure delivery.
From concepts to systems
Go beyond model names: reason about architectures, modalities, alignment, and capability limits—then apply them to real workflows.
Production-first practice
Evaluate, ground, deploy, and monitor LLMs using pragmatic patterns like RAG, safety layers, observability, and versioning.
Responsible by design
Incorporate ethics, governance, and security controls into the build—not after launch—so outputs remain trustworthy and auditable.
Exam Curriculum
The CRAIS certification covers a comprehensive curriculum designed to equip professionals with the knowledge and skills to navigate the complex landscape of responsible AI.
Foundations of Generative AI
Evolution: expert systems → ML → deep learning → transformers → GenAI LLM architectures (conceptual, vendor-neutral) & attention mechanism basics Pretraining, fine-tuning, and RLHF alignment fundamentals Modalities: text, code, image, audio, video, and multimodal systems Common use cases and limitations (hallucinations, stale knowledge, constraints)
Prompt Engineering & Interaction Design
Prompt patterns: zero/few-shot, role-based, chain prompts, structured outputs Advanced prompting: context windows, memory, multi-turn chains, reflection Constraints & formatting: schemas, delimiters, and instruction hierarchy Reducing bias and injection risk through design + guardrails Use cases across HR, finance, education, and support workflows
LLMs in Production
Adaptation strategies: full fine-tuning, LoRA, adapters, and RAG System integration: APIs, vector databases, orchestration workflows Evaluation: automated metrics + human eval, truthfulness & usefulness checks Deployment trade-offs: latency, cost, hallucinations, reliability, monitoring Case-driven thinking across regulated and enterprise environments
Responsible & Ethical Generative AI
Risk landscape: hallucinations, bias, toxicity, misinformation, IP concerns Mitigations: grounding, guardrails, moderation, human-in-the-loop review Governance concepts and standards-aligned thinking (risk-based oversight) Transparency, consent, and accountability in real deployments Fairness evaluation and documentation practices (model cards, dataset notes)
Security, Privacy & Compliance in LLMs
Privacy fundamentals: PII handling, minimization, anonymization, retention Threats: prompt injection, data exfiltration, model inversion, adversarial inputs Secure architecture: isolation, least privilege, key management, validation Monitoring & red-teaming for jailbreaks and abuse patterns Compliance-minded delivery for sensitive domains and organizational controls
Future of Generative AI & Career Applications
Agentic systems: planning, tool-use, and multi-step workflows Multimodal trajectory and enterprise product patterns Designing solutions balancing value, performance, cost, and risk Career pathways: engineer, consultant, strategist, product roles Capstone-style thinking: safeguards, oversight, user controls, escalation
Syllabus Weightage
Sample Examination
Experience the scenario-based methodology used in GIofAI professional assessments.
Tip: GIofAI scenario questions often present multiple technically "valid" options. You must choose the one that aligns best with the FATE (Fairness, Accountability, Transparency, and Explainability) framework and institutional risk policy.
GIofAI Exam Portal
A world-class testing interface designed for precision, security, and accessibility.
Clean, Distraction-Free UI
Our portal uses a high-contrast, minimalist design to help you focus on complex case studies.
Integrated Utility Tools
Access on-screen scientific calculators, fairness metric scratchpads, and translation assistants.
Safe Exam Browser
Institutional-grade lockdown technology ensures the integrity of your professional designation.
Flexible Session Management
Auto-save features and interruption-recovery protocols protect your progress against connectivity drops.
Logistics & Compliance
Global delivery with institutional security standards.
Exam Format
Retake Policy
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Up to 2 retakes permitted within a 6-month period.
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Each retake requires a separate examination purchase.
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Practice Exam completion is strongly recommended prior to retake attempts.
Scoring & Results
No Negative Marking
Candidates are encouraged to answer all 250 questions; there is no penalty for incorrect answers.
Instant Provisionals
View your provisional result immediately upon submitting your examination session.
Official Transcript
Verified certificate and transcript will be available in your dashboard within 48 hours.
Verified Identity
Biometric and government ID verification is mandatory for all candidates via the web-based portal.
Transcript & Certificate
Official documentation of your certification achievement.
Official Transcript
Your verified transcript includes detailed performance metrics and section-wise scores.
Digital Certificate
Your professional certification credential, verifiable and shareable.
Frequently Asked Questions
Practical answers, in plain language.
CGAIS is a multiple-choice assessment with both single-correct and scenario-based MCQs. The intent is to validate practical judgment across real deployment situations.
No. The curriculum emphasizes vendor-neutral understanding: capabilities, limitations, and trade-offs across model families and deployment approaches.
Focus on patterns like RAG, evaluation methods, monitoring, safety layers, prompt injection mitigations, least-privilege architecture, and privacy-minded logging and retention.
It typically includes identity verification and session monitoring to uphold integrity. Ensure you have a stable internet connection and a quiet environment consistent with proctored testing.
You can retake after a cooldown period. Retakes may use refreshed question sampling and always align to the current blueprint, so review your domain breakdown and target weaker areas.
Register for CGAIS — validate production-ready GenAI capability.
Join a certification path that prioritizes real-world judgment: interaction design, deployment trade-offs, evaluation, governance, and secure delivery.
Career Path
A progression from entry to leadership, aligned with certifications.
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Career Growth Ladder
| Level | Typical Roles | Relevant Certifications |
|---|---|---|
| Entry-Level | AI Technician, Data Analyst | CAIP |
| Mid-Level | AI Engineer, ML Engineer, AI Developer | CAIE, CRAIS, CGAIS |
| Advanced | AI Architect, AI Consultant, Security Specialist | CLLMS, CCVE, CAICE, CAISPS, Industry Certs |
| Leadership | Head of AI, AI Product Manager, CAIO | CAIL, CAITO, CAIGR, CEAIR |
Certification Pathway
GlofAI Learning Path (Certification Progression)
Stage 1 — Foundation
Stage 2 — Core AI Competency
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
Stage 1 — Foundation
Goal: Build baseline AI knowledge and confidence.
Certified AI Practitioner (CAIP) – Foundational skills in AI, data, and ML.
Stage 2 — Core AI Competency
Goal: Master model development and deployment.
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
Goal: Deep dive into specific domains of AI technology.
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
Goal: Apply AI to real-world sectors.
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
Goal: Lead AI strategy and enterprise adoption.
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
| Level | Focus | Example Certifications |
|---|---|---|
| Beginner | Foundations | CAIP |
| Intermediate | Core AI Skills | CAIE, CRAIS, CGAIS |
| Advanced | Specialized Technical | CLLMS, CCVE, CAICE, CAISPS |
| Expert | Industry Applications | CAIFS, CAIH, CAIRSC, CAIM, CAIPG |
| Leader | Strategic & Enterprise | CAIL, CAITO, CEAIR, CAIGR |