Developers Program
Generative AI
- 4.721 students
- Last updated : March 6, 2026
Overview
The Generative AI Developers Program is designed for learners who want to build modern AI applications using large language models (LLMs), vector databases, prompt engineering, and retrieval-augmented generation (RAG). This program focuses on hands-on implementation, helping developers, engineers, and AI enthusiasts gain the skills required to create production-grade GenAI systems.
You will learn how to integrate LLMs, build automation workflows, fine-tune models, and deploy AI-powered applications used in real businesses today.
This program is ideal for software developers, data scientists, ML engineers, and working professionals transitioning into Generative AI roles.
Program Objective
- Learn the core concepts behind LLMs, embeddings, vector search, and prompt engineering.
- Build practical GenAI applications such as chatbots, document assistants, Q&A systems, and AI automation workflows.
- Implement Retrieval-Augmented Generation (RAG) for enterprise use-cases.
- Fine-tune models using LoRA and domain-specific datasets.
- Integrate GenAI into applications using APIs, SDKs, and libraries like LangChain & LlamaIndex.
- Deploy production-ready GenAI systems using Docker, FastAPI, and cloud platforms.
What You Will Learn
- Introduction to LLMs & generative architectures
- Tokenization, embeddings, vector similarity
- Understanding model types (GPT, Llama, Claude, Mistral, Gemini)
- Prompt engineering fundamentals
- Chatbot development (rule-based + LLM-based)
- Building text, content, and code generators
- Designing prompt templates, chains, and tools
- Function calling and agent workflows
- Working with APIs (OpenAI, Claude, Llama, Gemini, etc.)
- Embeddings creation & vectorization
- Vector databases: FAISS, Pinecone, Weaviate, ChromaDB
- Chunking & indexing strategies
- Document Q&A systems for PDFs, websites, files
- Building enterprise-grade RAG pipelines
- Improving accuracy, reducing hallucination
- LoRA, QLoRA & parameter-efficient fine-tuning
- Dataset preparation & labeling
- Instruction tuning & domain adaptation
- Evaluating fine-tuned models
- Hosting models locally or on cloud
- API deployment via Flask/FastAPI
- AI microservices using Docker
- GPU/CPU inference optimization
- Model monitoring (latency, drift, hallucinations)
- CI/CD pipelines for GenAI applications
- Deploying on AWS, Azure, GCP, Hugging Face Spaces
Tools & Technologies Covered
GPT, Claude, Llama, Mistral, Gemini
LangChain, LlamaIndex, Transformers, Hugging Face Hub
Pinecone, FAISS, ChromaDB, Weaviate
PyTorch, TensorFlow (optional)
FastAPI, Flask, Docker
AWS / Azure / GCP
Git, MLflow, Streamlit / Gradio
Projects You Will Build
Projects cover domains such as: healthcare, HR, finance, logistics, retail, education, and legal.
Career Outcomes
After completing the program, learners can pursue roles such as:
Why Learners Choose This Program
- Hands-on development-focused curriculum
- Work on industry-level GenAI projects
- Master RAG, fine-tuning, and deployment — the skills employers need today
- Learn with modern LLM tools and frameworks
- Beginner-friendly but deep enough for engineers
- Guidance to build a strong AI portfolio
Instructor
Data Science Expert
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