Overview
Generative AI is reshaping the way humans and machines interact. Understanding its core terminologies is essential for anyone entering the AI space, whether as a developer, tester, product manager, or business professional.
This course provides foundational to advanced insights into Large Language Models (LLMs), model architectures, prompting strategies, optimization techniques, and real-world deployment concepts. Learners will explore key terminologies—ranging from tokenisation and transformers to reasoning models and multimodal systems—through simplified explanations, visuals, and examples.
The program is structured to support aspiring professionals enrolling from different regions, including those looking for a Generative AI course for beginners in Sydney or a comprehensive Generative AI language course in Australia.
By the end of the program, participants will gain a strong conceptual understanding of how modern generative AI systems operate and the technologies that power them.
Duration of Generative AI Language Course
The course is valid for ?? days (Online Portal)
Extended learning resources and bonus recorded sessions are provided to ensure participants can revisit complex Generative AI concepts throughout the duration.
Location
Online (Self-paced modules with videos, quizzes, and assignments)
Learners from anywhere in Australia, especially Sydney, can benefit from this fully remote structure, making it one of the most accessible Generative AI concepts training programs in the region.
Intended audience
- Professionals aiming to understand the technical language of AI.
- Students and graduates from IT, Data Science, or related disciplines.
- Testers, developers, and analysts working with AI-integrated systems.
- Managers and decision-makers seeking AI literacy to support project discussions.
- Educators and content creators looking to simplify AI concepts for learners.
- Individuals seeking a Generative AI course for beginners in Sydney or Australia-wide professionals transitioning into AI-centric roles.
Detailed program structure
COURSE OVERVIEW
- Introduction to Generative AI Terminologies
- Scope and Learning Outcomes
- The Evolution of Generative AI and Modern AI Ecosystem
FOUNDATION CONCEPTS IN GENERATIVE AI
- What is Generative AI?
- Overview of AI, Machine Learning, and Deep Learning
- The Role of Large Language Models (LLMs)
- How AI Understands and Generates Human-like Language
CORE TERMINOLOGIES EXPLAINED
Large Language Models (LLMs)
- Definition and Function
- Role in Natural Language Processing (NLP)
- Real-world Applications (Chatbots, Translation, Content Creation)
- Interactive Example Walkthrough
Tokenisation
- Understanding Tokens: Words, Subwords, and Characters
- How Text is Broken Down for Model Processing
- Importance of Tokenisation in Language Understanding
Vectorisation
- Representing Text as Numerical Data
- Vectors and Semantic Meaning
- Role in Similarity Search and Context Matching
Attention Mechanism
- What Attention Does and Why It Matters
- Resolving Ambiguity in Sentences
- How Attention Improves Model Accuracy
Self-Supervised Learning
- Definition and Advantages
- Difference Between Supervised and Self-Supervised Learning
- Scalability and Data Efficiency
Transformers
- Architecture and Core Components
- Self-Attention in Transformers
- Why Transformers Revolutionised AI
MODEL OPTIMISATION & CUSTOMISATION
Fine-Tuning
- Purpose and Process
- Domain-Specific Adaptation (Healthcare, Finance, etc.)
- Example Scenarios
Few-Shot Prompting
- What Few-Shot Prompting Is
- How It Improves Responses During Inference
- Example with Contextual Queries
Retrieval-Augmented Generation (RAG)
- Combining LLMs with External Knowledge Sources
- Retrieval Methods (Vector DB, Graph DB, Cache)
- Real-Time Context Augmentation
Vector Databases
- Purpose and Functionality
- Similarity Search and Context Retrieval
- Algorithms like HNSW and Their Roles
ADVANCED MECHANISMS AND ARCHITECTURES
Model Context Protocol (MCP)
- How External Context Is Integrated
- Real-Time Data Fetching and Decision-Making
- Use Case: Automated Flight Booking
Context Engineering
- Designing Input for Improved Response Quality
- Techniques: Sliding Window, Keyword Extraction, Summarization
- Distinction from Prompt Engineering
- Real-World Example Workflow
Agents
- What AI Agents Are and How They Work
- Capabilities: Multi-Step Task Automation, Real-Time API Access
- Example: Automated Travel Agent
Reinforcement Learning with Human Feedback (RLHF)
- Definition and Process
- Analogy and Limitations
- Role in Improving Model Behaviour
REASONING AND COGNITIVE AI CONCEPTS
Chain of Thought
- Step-by-Step Reasoning Approach
- Benefits and Practical Examples
Reasoning Models
- What They Are and How They Differ from LLMs
- DeepSeek, OpenAI O1/O3 as Examples
- Structured Logical Thinking (Chain, Tree, Graph of Thought)
MULTIMODAL & COMPACT MODELS
Multimodal Models
- Definition and Capabilities
- Text, Image, Audio, and Video Integration
- Benefits and Real-World Use Cases
Small Language Models (SLMs)
- Compact Models for Specific Tasks
- Benefits: Cost, Privacy, Speed
- Use Cases: Internal Tools, Sales Bots, Domain Assistants
MODEL COMPRESSION AND DEPLOYMENT TECHNIQUES
Distillation
- Transferring Knowledge from LLMs to SLMs
- Teacher-Student Learning Process
- Efficiency and Resource Optimization
Quantisation
- Reducing Model Precision for Faster Computation
- Running AI on Edge Devices and Low-Resource Systems
- Combining with Distillation for Optimal Performance
CAPSTONE MODULE: INTEGRATING TERMINOLOGIES
- Connecting All Terms into an End-to-End AI Workflow
- Example: How Input Text Flows Through Tokenisation → Vectorisation → Attention → Transformer → Output Generation
- Mini Quiz and Case Study
PROFESSIONAL DEVELOPMENT
- AI Literacy for Career Growth
- Communication of AI Concepts to Non-Technical Teams
- Industry Trends: From LLMs to Reasoning Models
- Interview and Certification Preparation
Highlights Of The Generative AI Concepts Training Program:
- Case Study – Hands-on training on real applications
- Multiple Practical Labs and Workshops
- Scenario-based Practical Assessments
- Post-Training Assessment Test (90 min)
- Training Certificate from QTechEd (powered by Adactin Group)
- Feedback Survey form
- Specialised Trainers to answer the query
- Support on Mock Interviews
- Career Counselling and Placement Assistance
- Connected with other industry standard materials
- Alumni Network Access
- 12 Months Content Access
Bonus: Additional modules tailored for learners enrolling in a Generative AI course for beginners in Sydney and those seeking structured Generative AI concepts training across Australia
FAQs
- Why does understanding terminology matter before learning to build AI systems?
Most learners struggle not with concepts, but with the language used to describe those concepts. By mastering terminology first, you accelerate clarity, reduce confusion, and become far more confident when working with advanced tools later.
- Will this course help me if I’m overwhelmed by technical jargon?
Absolutely. The program breaks down complex ideas using analogies, visuals, and guided examples, helping even non-technical learners make sense of AI terminology without feeling lost.
- How is this course different from typical AI training programs?
Instead of jumping directly into coding or models, it focuses on the conceptual building blocks that drive modern AI. This foundation allows learners to later specialise in any branch of AI with much greater ease.
- Do I need any prior experience with programming to understand the lessons?
No. The content is designed so that anyone, regardless of technical background, can grasp the principles. All explanations prioritise simplicity, clarity, and real-world relevance.
- Will the course help me communicate more effectively in AI-related roles?
Yes. One major outcome of the program is improving your ability to discuss AI concepts confidently with teams, clients, and stakeholders, enhancing your professional value.
