Generative AI Fundamentals
How large language models work, what they can and cannot do, and how to apply them responsibly in business.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Sat, 21 Nov 2026 · 24 hours · 3 days
Generative AI Fundamentals
Duration: 24 hours · 3 days
Next batch: Sat, 21 Nov 2026
Format: Online live · Bengaluru classroom
Modules: 8 · Topics: 38
Level: Beginner
Fee: ₹10,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Generative AI Fundamentals | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| Generative AI Fundamentals | Mon, 04 Jan 2027 | Bengaluru classroom | Weekday | 3 days | Enquire |
| Generative AI Fundamentals | Sat, 13 Feb 2027 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
Soaring demand, and where it takes you
Almost every function is exploring generative AI, so a shared, accurate understanding across teams has become a basic requirement.
Become an expert
Roles this programme prepares you for:
Become a GenAI-literate Manager
Evaluates AI proposals and sets realistic expectations.
- Explain how LLMs are trained and generate text
- Distinguish prompting, RAG and fine-tuning
- Recognise common GenAI risks such as hallucination and data leakage
About the course
A clear, hype-free foundation in generative AI: how large language models work, where they add value, how retrieval and fine-tuning differ, and how to use them responsibly. Suitable for mixed technical and business audiences.
How large language models work, what they can and cannot do, and how to apply them responsibly in business.
Why should I attend?
Managers and team leads
Business analysts and product owners
Engineers new to GenAI
L&D, HR and operations professionals
Prerequisites
None; curiosity about AI and basic computer skills
Level
Beginner
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 24 hours · 3 days
Course fee (open batch): ₹10,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Explain how LLMs are trained and generate text
- Distinguish prompting, RAG and fine-tuning
- Recognise common GenAI risks such as hallucination and data leakage
- Write effective prompts for business tasks
- Evaluate GenAI use cases for value and feasibility
- Understand responsible-AI and governance basics
Skills you will gain
Concepts
Application patterns
Responsible AI
Tools and platforms covered
ChatGPT
Hugging FaceOpenAI API
Python
JupyterCourse curriculum
M01 What generative AI is
- Predictive vs generative AI
- Foundation models
- Text, image and multimodal models
- How models are trained and aligned
- Open vs closed models
🧪 Hands-on: Explore several model types
M02 How LLMs work
- Tokens and next-token prediction
- Transformers in plain English
- Context windows
- Embeddings explained simply
- Temperature and sampling
🧪 Hands-on: Visualise tokenisation
M03 Prompting well
- Instructions, context and examples
- Structured output
- Iteration
- Role prompting and personas
- Common prompt anti-patterns
🧪 Hands-on: Prompt clinic on business tasks
M04 RAG, fine-tuning and agents
- Retrieval-augmented generation
- When to fine-tune
- What agents add
- Tool use and function calling
- Choosing between approaches with a decision guide
🧪 Hands-on: Try a simple RAG demo
M05 Risks and governance
- Hallucination
- Privacy and IP
- India's DPDP Act at a glance
- Bias and fairness
- Responsible-AI principles in practice
🧪 Hands-on: Risk review of a sample use case
M06 Use-case workshop
- Value vs feasibility
- Data readiness
- Pilot design
- Build vs buy
- Estimating cost and risk
🧪 Hands-on: Shortlist use cases for your team
M07 Hands-on with GenAI tools
- Chat assistants for knowledge work
- Image generation basics
- No-code RAG over documents
- Comparing model outputs
🧪 Hands-on: Build a no-code document Q&A and compare two models
M08 Capstone: your first GenAI use case
- Problem statement
- Prototype
- Risk review
- Presentation
🧪 Hands-on: Present a prototype and pilot plan for a real team use case
Hands-on projects
Use-case canvas
A one-page assessment of a GenAI idea for your team.
Prompt playbook
Tested prompts for three common tasks.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Generative AI Fundamentals. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Is this technical?
It is suitable for both technical and non-technical participants; optional notebooks are available for engineers.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹10,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
24 hours · 3 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
A clear, hype-free foundation in generative AI: how large language models work, where they add value, how retrieval and fine-tuning differ, and how to use them responsibly. Suitable for mixed technical and business audiences.
What is Generative AI Fundamentals?
How large language models work, what they can and cannot do, and how to apply them responsibly in business.
Who this course is for
Managers and team leads, Business analysts and product owners, Engineers new to GenAI, L&D, HR and operations professionals.
What you will be able to do
Explain how LLMs are trained and generate text. Distinguish prompting, RAG and fine-tuning. Recognise common GenAI risks such as hallucination and data leakage. Write effective prompts for business tasks. Evaluate GenAI use cases for value and feasibility. Understand responsible-AI and governance basics.
Career paths
Roles this course supports include GenAI-literate Manager, AI Business Analyst, Junior AI Developer. Almost every function is exploring generative AI, so a shared, accurate understanding across teams has become a basic requirement.
AI & GenAI guides
Generative AI vs Traditional AI: What Is the Difference?
Traditional AI mostly predicts or classifies. Generative AI produces new content. Both have a place in the enterprise.
What Is Prompt Engineering?
Prompt engineering is the practice of writing instructions that get reliable, useful output from a language model.
What Is ChatGPT and How Does It Relate to Other AI Assistants?
ChatGPT is a conversational AI assistant from OpenAI, built on large language models. It is one of several similar assistants.
How to Compare AI Assistants for Your Team
Several general-purpose AI assistants compete for enterprise use. Rather than chasing rankings, evaluate them against your own needs.