Build AI applications using LLMs, prompt engineering, RAG and vector databases.
Build AI applications using LLMs, prompt engineering, RAG and vector databases.
Learn from experienced trainers who guide you step by step, from the basics to real projects.
Build hands-on projects based on real-world use cases to apply what you learn and strengthen your portfolio.
Get help with resume building, interview preparation and job search so you are ready for the job market.
Get an opportunity to gain practical, hands-on experience through an internship after the course.
Receive a certificate on completing the course to showcase your skills to employers.
Take part in a face-to-face interview with industry experts to get feedback and build confidence for real interviews.
Generative AI is changing how software is built. In this course you will learn how large language models work, how to guide them with prompt engineering, and how to connect them to your own data using embeddings, vector databases and RAG. The course ends with real-world AI application projects that you build and deploy.
Python
LLM APIs
LangChain
Vector Databases
Hugging Face
Streamlit
FastAPI
GitHubBuild an assistant that writes, summarises and rewrites text using well-designed prompts.
What you will do
Skills you will use
A working assistant that writes, summarises and rewrites text on request.
Let users ask questions about their own PDFs and notes using embeddings and a RAG pipeline.
What you will do
Skills you will use
A chatbot that answers questions from your own documents.
Create a semantic search tool over company-style documents with a vector database.
What you will do
Skills you will use
A search tool that finds relevant documents by meaning, not just keywords.
Take a RAG application from idea to a deployed app with a clean interface, then present your results.
What you will do
Skills you will use
A deployed RAG application with a clean interface, presented to an audience.
Start your Data and AI journey with Python, Statistics, SQL and machine learning.
Real generative AI projects and a certificate that help you stand out in campus and off-campus interviews.
A smooth step from data science to building applications with large language models.
The skills to build, evaluate and deploy AI applications that work with your own data.
The ability to turn your Python skills into working generative AI applications.
Prepare to take your skills into the job market through:
Knowing Python basics is a plus (functions, data types, files and basic OOP). Working with APIs and JSON and basic machine learning concepts are helpful. If you are new to Python, start with Course 1: Python for Data Science & Machine Learning.
Degree freshers (BCA, BSc CS, BE/BTech, MCA, MSc CS, MTech), Intermediate Track students ready for cutting-edge AI, working professionals and developers moving into AI roles, and Python learners who want to build real applications with LLMs.
Four months, planned as a 16-week roadmap from generative AI and prompt engineering to RAG systems and a deployed capstone project.
Python, LLM APIs, LangChain, vector databases, Hugging Face, Streamlit and FastAPI, with GitHub for your project work.
Yes. You receive a certificate on completion, and an internship opportunity is part of the program.
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