About this course
A practical starting point for GenAI: how LLMs work, prompting, embeddings, working with hosted and open-weight models, fine-tuning basics, and building your first apps end to end.
What you will be able to do
- Explain how transformers and LLMs generate text
- Write prompts that are reliable and testable
- Use embeddings for search and similarity
- Run open-weight models locally and via APIs
- Fine-tune a small model with LoRA
Curriculum
01How LLMs work
- Tokens
- Attention
- Sampling
02Prompting
- Patterns
- Few-shot
- Testing prompts
03Embeddings
- Similarity
- Vector stores
- Semantic search
04Open models
- Hugging Face Hub
- Running locally
- Quantisation
05LangChain essentials
- Chains
- Retrievers
- Output parsers
06Fine-tuning basics
- When to fine-tune
- LoRA
- Evaluation
Tools you will use
Prerequisites
- Python basics
Projects you will build
5 milestones
AI career suite: resume, cover letter and interview prep
Paste a job post and your resume; get a tailored resume, a cover letter, a skills-gap list and practice questions.
LangChain · Gemini / OpenAI · Streamlit Coming soon
5 milestones
Chat with any codebase
Point it at a GitHub repository and ask how things work — “where is auth handled?” — with answers linked to files and lines.
LangChain · Tree-sitter · Chroma Coming soon
5 milestones
Automated candidate interview and evaluation system
Generate role-specific questions, run a structured text interview and produce a scored, evidence-backed evaluation for the hiring team.
LangChain · Streamlit · SQLite Coming soon
5 milestones
GenAI-powered clothing store with Pydantic AI
A fashion storefront with a stylist assistant that understands “something for a beach wedding under ₹3,000” and builds an outfit from real stock.
Pydantic AI · FastAPI · PostgreSQL Coming soon