Who Is This For

Is This Course Right For You?

🔥

Data Engineers

Add GenAI capabilities to your pipelines — AI-powered ETL, data quality and enrichment.

📊

Data Analysts

Use LLMs and AI functions to automate analysis, reporting and natural language queries.

🤖

ML Engineers

Extend ML workflows with LLMs, fine-tuning, RAG and agentic AI patterns.

☁️

Cloud Architects

Design enterprise AI platforms on Databricks, AWS Bedrock or Azure OpenAI.

🎓

Freshers & Students

GenAI skills command ₹15–40 LPA — one of the fastest-growing job categories.

🏢

Product Managers

Build AI-powered features into products using LangChain and RAG pipelines.

Tools Covered

🤖 OpenAI GPT-4

🧠 LangChain

🔍 Vector Search

📊 Databricks Mosaic AI

🗄️ Chroma / Pinecone

🔥 Databricks

📨 LlamaIndex

🤗 Hugging Face

⚙️ LangGraph

🌐 AWS Bedrock

🔷 Azure OpenAI

💬 Prompt Engineering

🛠️ Function Calling

📋 RAG Pipelines

Course Curriculum

8 Modules — Key Concepts

Here are the core topics you'll master. Each module includes hands-on labs with real GenAI for Data access.

Module 01

LLMs & Foundation Models

What are LLMs — GPT, Claude, Gemini, Llama architecture
Transformer architecture basics for practitioners
Tokenization, context window, temperature settings
OpenAI API — completions, chat, embeddings
Choosing the right model for your use case

Module 02

Prompt Engineering

Zero-shot, few-shot, chain-of-thought prompting
System prompts and persona engineering
Structured output with JSON mode
Prompt templates and versioning
Evaluating prompt quality — BLEU, ROUGE, LLM-as-judge

Module 03

RAG Pipelines — Retrieval Augmented Generation

Why RAG? — grounding LLMs with your own data
Document loading — PDF, CSV, web, databases
Text chunking strategies — fixed, recursive, semantic
Embedding models — OpenAI, HuggingFace, sentence-transformers
Vector stores — Chroma, Pinecone, Databricks Vector Search

Module 04

LangChain & LangGraph

LangChain chains — LCEL syntax and composition
LangChain agents — ReAct, Tool-calling patterns
LangChain memory — conversation history patterns
LangGraph — stateful multi-agent workflows
LangSmith — tracing and evaluation

Module 05

Databricks Mosaic AI & GenAI

Mosaic AI Gateway — LLM endpoint management
Databricks Vector Search on Delta Lake
AI Functions — ai_query(), ai_classify() in SQL
MLflow AI tracing — experiment tracking for LLMs
Databricks Model Serving — deploy LLMs as APIs

Module 06

Agentic AI & Production

AI Agents — planning, tool use, memory
Multi-agent systems with LangGraph
Guardrails and safety for production AI
LLM observability — cost, latency, quality monitoring
AI pipeline integration with Airflow/Databricks Workflows

M01

LLMs & Foundation Models Fundamentals

⏱️ 5 Hours● Beginner

▾

What are Large Language Models (LLMs)

Transformer architecture — attention mechanism basics

GPT, Claude, Gemini, Llama — comparison and use cases

OpenAI API — authentication, completions, chat completions

Tokens, context windows, temperature, top_p settings

Embeddings — what they are and how to use them

LLM pricing and cost estimation

🔬 OpenAI API First App📝 Quiz: LLM Fundamentals

M02

Prompt Engineering

⏱️ 5 Hours● Beginner

▾

Zero-shot prompting — direct task instructions

Few-shot prompting — examples in context

Chain-of-thought prompting — step-by-step reasoning

System prompts and role-based prompting

Structured output with JSON mode and function calling

Prompt templating with LangChain PromptTemplate

Prompt versioning and management

Evaluation — LLM-as-judge, BLEU, ROUGE

🔬 Prompt Engineering Workshop📝 Quiz: Prompt Techniques

M03

RAG Pipelines — Core Architecture

⏱️ 8 Hours● Intermediate

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RAG architecture — retrieval + generation pattern

Document loaders — PDF, CSV, web scraping, databases

Text splitting — fixed chunk, recursive, semantic

Embedding models — OpenAI, HuggingFace sentence-transformers

Vector stores — Chroma, FAISS, Pinecone, Databricks Vector Search

Similarity search — cosine, dot product, MMR

Reranking — cross-encoder rerankers

Query transformation — HyDE, multi-query

Evaluation — Ragas framework for RAG quality

🔬 Build a PDF RAG Chatbot🏗️ Project: Enterprise Knowledge Base RAG

M04

LangChain & LangGraph

⏱️ 7 Hours● Intermediate

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LangChain LCEL — chain composition syntax

LLMChain, SequentialChain, RouterChain

RetrievalQA and ConversationalRetrievalChain

LangChain Memory — Buffer, Summary, Window memory

LangChain Agents — ReAct, OpenAI Functions

Tool creation — custom tools for agents

LangGraph — stateful agent orchestration

LangSmith — tracing, evaluation, dataset management

🔬 LangChain Agent Build🏗️ Project: Multi-Tool AI Agent

M05

Databricks Mosaic AI & GenAI Platform

⏱️ 7 Hours● Advanced

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Databricks AI Gateway — manage LLM endpoints

Databricks Vector Search on Delta Lake

AI Functions — ai_query(), ai_classify(), ai_extract() in SQL

MLflow AI Tracing — LLM experiment tracking

Databricks Model Serving — deploy custom and FHIR models

Databricks Playground — test models

Unity Catalog integration for AI assets

LLM fine-tuning on Databricks

🔬 Databricks RAG on Delta Lake🏗️ Project: RAG Chatbot on Databricks

M06

Fine-tuning & Custom Models

⏱️ 5 Hours● Advanced

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When to fine-tune vs RAG vs prompt engineering

LoRA and QLoRA — parameter efficient fine-tuning

Hugging Face Transformers — training pipeline

PEFT — Parameter Efficient Fine-Tuning techniques

Evaluation — perplexity, task-specific metrics

Fine-tuning on Databricks with MLflow tracking

Serving fine-tuned models as REST APIs

🔬 Fine-tune a Small LLM

M07

Agentic AI & Multi-Agent Systems

⏱️ 7 Hours● Advanced

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What are AI Agents — ReAct pattern

Function calling and tool use patterns

Memory systems — short-term, long-term, episodic

LangGraph — multi-agent stateful workflows

CrewAI — role-based agent orchestration

AutoGen — conversational multi-agent systems

Guardrails — safety and content moderation

Production agentic pipelines with Airflow/Databricks

🔬 Multi-Agent Data Pipeline🏗️ Project: Agentic Data Analyst

M08

GenAI Projects & Career Prep

⏱️ 6 Hours● Advanced

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Project 1 — Enterprise RAG: Internal docs chatbot with Databricks

Project 2 — AI Data Quality: LLM-powered anomaly detection

Project 3 — Multi-Agent System: Automated data analyst

Project 4 — Production GenAI API with monitoring and guardrails

GenAI job market — roles, skills, salary expectations

Resume writing for GenAI engineer roles

Interview prep — top 40 GenAI interview questions

🏗️ 4 Real GenAI Projects📝 Interview Prep

Master Generative AI for Data Engineering & Analytics

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8 Modules — Key Concepts

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