Generative AI for Students
LLM fundamentals, prompting, RAG and applied workflows — the AI literacy every placement and product role expects
Generative AI stopped being a novelty and became a baseline skill on job descriptions. This course gives you the mental model and the working habits: how language models actually predict text, why they hallucinate, how to write prompts that behave like briefs, how retrieval-augmented generation grounds answers in real documents, and how to evaluate, cost and secure a GenAI feature.
Four modules, eight lessons on the nine-step standard: objective, hook, concept, worked example, practice, common mistakes, exam angle, server-graded quiz and revision summary. A 30-question bank feeds the timed final exam; passing earns a verifiable TieEdu certificate.
Who this is for: students and early-career engineers who use chatbots daily but cannot yet explain tokens, context windows or RAG — and who need repeatable workflows for research, writing, coding and placement prep without getting burned by fabricated citations.
What you need: access to any mainstream chat assistant for the practice prompts. No coding is required, though Module 3 shows how engineers wire models into products.
What you will be able to do
- Explain next-token prediction
- tokens
- context windows and temperature
- Diagnose hallucination and apply grounding
- RAG and verification mitigations
- Write prompts using the role/task/constraints/format skeleto
- Chain prompts and add critic passes for complex tasks
- Describe a RAG pipeline and choose sensible chunking and ret
- Build a golden eval set and track token cost for a GenAI fea
- Defend against prompt injection and handle PII with practica
- Run safe workflows for research
- writing
What is included
- 4 modules
- 8 lessons
- 1 hr 25 min of material
- Certificate on completion
₹109