Generative AI Learning Path
Route: /learning-paths/generative-ai/
Status: Working content draft for review
Purpose: Content architecture, copy direction, SEO starting points, and page-level placeholders
Source basis: WINNOW learning catalogue and Debojyoti Biswas profile documents supplied for the project
Important: Claims, dates, pricing, batch schedules, testimonials, placement outcomes, institutional clients, and certifications must not be published unless verified and approved.
Page purpose
Guide a goal-oriented visitor toward the most relevant WINNOW programme or sequence.
SEO starting point
- Generative AI learning path
- LLM learning path
- learn transformers and RAG
- Generative AI course from fundamentals
- LLM foundations course
Tentative content hierarchy
- Hero
- Who this path is for
- Recommended sequence
- Capability progression
- Where to enter based on experience
- Relevant programmes
- Instructor credibility
- CTA
Draft content
Hero
Generative AI Learning Path
A structured way to choose the relevant WINNOW programme based on what you want to be able to do, rather than on course names alone.
This path is for serious learners who want to understand how modern Generative AI systems work beneath the application layer.
Recommended preparation
Learners should have enough comfort with Python and basic quantitative reasoning to follow the technical progression.
Where those foundations are missing:
- Groundwork can provide Python/data fundamentals.
- Additional mathematics preparation may be required depending on the learner.
Flagship programme
The GenAI Odyssey
The programme progresses through:
- mathematics essentials
- Python
- AI/ML ecosystem
- text representation
- embeddings
- neural network foundations
- sequence models
- attention
- transformers
- fine-tuning
- decoding
- RAG and vector databases
- model evaluation and risk
Positioning note
Do not market this path primarily as a prompt-engineering course. Its strongest distinction is technical grounding from first principles through modern LLM architecture and applied RAG.
Entry-point guidance
[CONTENT REQUIRED: simple “Start here if…” rules based on instructor-approved prerequisites]
Related programmes
Use cards linking to the specific course pages, not duplicated full curricula.
CTA
Need help choosing an entry point?
CTA: Discuss Course Fit