Nishi Singh
Research Scientist, PhD
Research scientist bringing scientific rigour and research methodology to AI problem-solving.
Full bio →
Live Cohort • Beginner Friendly
Generative AI & AI Application Development
Learn AI. Master GenAI tools. Build real-world AI applications. A 42-hour, project-based Generative AI course covering LLMs, prompt engineering, RAG, AI agents, and production deployment — taught by engineers who build AI systems for a living.
42 Hours
Duration
30 hrs learning + 12 hrs hands-on
Job Ready
Industry-relevant skills
Mapped to real GenAI job postings
Basics → Advanced
Zero to production
LLMs, RAG, agents, deployment
3 Projects
Real-world AI builds
Portfolio-ready, not toy demos
Who this is for
Built for people who want to build, not just watch
This Generative AI course online is designed for beginners with basic Python familiarity, computer science students, and working professionals pivoting into AI roles. If you can write a simple function, you can start here.
Students
CS, engineering, and non-CS students who want practical AI skills that go beyond using ChatGPT — and a portfolio that proves it.
Developers
Software engineers who want to add LLMs, RAG, and AI agents to their toolkit and understand how production AI systems are actually built.
Career switchers
Professionals moving into GenAI Engineer, AI Product Engineer, or applied ML roles, looking for a structured, credible path in.
Curriculum overview
From AI fundamentals to production-ready GenAI apps
14 sessions across 42 hours. Every module below links to the full session-by-session breakdown, and several link to a deep-dive guide if you want to go further right now.
AI & GenAI Foundations (6 hrs)
What is AI vs ML vs Deep Learning vs Generative AI, how LLMs actually generate text, tokens, context windows, and the model landscape (GPT, Claude, Gemini, Llama, Mistral).
Read: What is an LLM? →Prompt Engineering (3 hrs)
Zero-shot to few-shot prompting, role & context prompting, structured JSON outputs, and reducing hallucinations through better prompts.
Read the full guide →AI Tools & Productivity (3 hrs)
Working effectively with ChatGPT, Claude, Gemini, AI coding assistants, and research tools like Perplexity and NotebookLM.
LLM App Development (4 hrs)
Calling LLM APIs from Python, building a FastAPI backend, streaming responses, and handling rate limits and retries like a real production service.
RAG & Knowledge Systems (4 hrs)
Vector databases, chunking, embeddings, and retrieval — building a "ChatGPT that knows your documents" pipeline end to end.
Read: What is RAG? →AI Agents & Tool Calling (3 hrs)
Turning an LLM into an agent with tool/function calling, agent loops, planning, and memory concepts.
Read: AI Agents Explained →Multimodal AI (2 hrs)
Image generation, vision models, speech-to-text and text-to-speech, and OCR/document intelligence.
Production AI (2 hrs)
Architecture, caching, authentication, logging, prompt versioning, cost management, and a live prompt-injection defence exercise.
Read the production guide →AI Career & Future (2 hrs)
AI/GenAI/ML engineering roles, real job postings mapped to your new skills, and building a portfolio that gets interviews.
Prep with interview questions →Projects & Capstone (12 hrs)
3 hands-on builds: an AI Study Assistant, a document-chat RAG app, and an AI Career Assistant capstone with tool calling + RAG combined.
See all 3 projects →What makes this course stick
Engagement systems that make sure you actually finish
Most online courses have a completion rate under 15%. Ours is designed against that failure mode.
Ship It ritual
The last 10 minutes of every build session: post a working screenshot or demo to the course group. Public momentum beats private procrastination.
Live leaderboard
Points for prompt battles, the fastest working RAG pipeline, and best demos — starting Session 4 and running through Demo Day.
"Break the AI" challenges
Students compete to make a model hallucinate, then a live prompt-injection challenge in Session 12 — you learn failure modes by causing them first.
Personal-stakes projects
You use your own notes, your own PDFs, and your own resume in every project — engagement goes up when the output is actually useful to you.
Public Demo Day
Every student presents their AI Career Assistant capstone to peers, faculty, and an invited industry guest in the final session.
Anchor analogies
One memorable analogy per module (tokens = LEGO bricks, RAG = open-book exam) repeated until it sticks — used throughout every session.
Hour accounting
Where the 42 hours go
| Component | Hours |
|---|---|
| AI & GenAI Foundations | 7 |
| Prompt Engineering | 3 |
| AI Tools & Productivity | 3 |
| LLM App Development | 4 |
| RAG & Knowledge Systems | 4 |
| AI Agents & Tool Calling | 3 |
| Multimodal AI | 2 |
| Production AI | 2 |
| AI Career & Future | 2 |
| Hands-on Projects & Capstone | 12 |
| Total | 42 |
Your instructors
Learn from people building AI systems right now
Satish Kumar
Software Development Manager, Amazon
Has worked on high-scale applied AI automation projects at Amazon.
Full bio →
Pravesh Dholwani
Applied AI Engineer
Scaled the AI and backend infrastructure powering products used by 10M users.
Full bio →
Nitish Kumar
AI Team Lead, ISRO
Leads the AI team at ISRO and brings deep applied-AI engineering experience to the classroom.
Full bio →Outcomes
What you walk away with
- ✓ A working understanding of how LLMs, RAG, and AI agents actually function — not just how to prompt them
- ✓ 3 deployable AI projects on your GitHub, each with a problem → architecture → tech → outcome writeup
- ✓ Hands-on experience with the exact stack used in production GenAI teams: Python, FastAPI, vector databases, LLM APIs
- ✓ A Certificate of Completion and a portfolio presentation you've already rehearsed on Demo Day
- ✓ A clear map of AI/GenAI job roles and the skills each one actually requires
FAQ
Common questions about the course
Students, early-career developers, and working professionals who want to build real AI applications — not just use ChatGPT. No prior machine learning experience is required; Session 0 gets everyone to the same starting line before Session 1 begins.
Basic familiarity with Python (variables, functions, loops) is helpful but not mandatory. The course starts with plain Python scripts before introducing frameworks, and starter templates are provided so you modify working code rather than starting from a blank file.
You'll ship 3 working AI applications during the course itself: an AI study assistant, a RAG-based "chat with your documents" app, and an AI career assistant capstone that combines tool calling, RAG, and external APIs — plus the skills to build your own AI product ideas afterward. See the full project breakdown on the projects page.
The course runs live and cohort-based (live, cohort-based, online), across 14 sessions of 3 hours each, so every concept is reinforced with live Q&A and instructor feedback — not pre-recorded videos.
Yes. Students who complete the capstone project and present on Demo Day receive a Certificate of Completion from Synaptech Innovations, along with a portfolio-ready GitHub repository and project writeups.
Python, FastAPI, LLM APIs (OpenAI, Gemini, Groq), the Chroma vector database, embeddings, and the concepts behind frameworks like LangChain (covered as "alternatives that exist" so you understand the ecosystem, not just one library). See the full session-by-session curriculum.
Three things: every concept is built the same week it's taught (not weeks later), the class runs live engagement systems — a leaderboard, "break the AI" hallucination-hunting, and a public Demo Day — and every instructor is a working AI/software engineer, not a course-only presenter. See who teaches the course.
Apply now
Seats are limited to keep every session hands-on
Tell us a bit about yourself and we'll get back to you with the next cohort's schedule, fees, and a short call to see if this course is the right fit.
- ✓ No obligation — this is a conversation, not a sales call
- ✓ We'll answer questions about background requirements and schedule
- ✓ Prefer email? Reach us directly at director@synaptechinn.com
Reserve your seat
Tell us a little about you — we'll get back within one business day.