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Generative AI Course — Build What Today’s Systems Use

This Generative AI course is a hands-on, engineering-first program that teaches you to build, fine-tune, and deploy production-grade Generative AI systems — not just use APIs.

From transformer internals to enterprise deployment, this course covers every layer of modern GenAI. You’ll graduate with a portfolio of real projects and the skills hiring teams actually test for.

Not no-code. Not prompt-only. Real GenAI engineering.

LLM EngineeringMultimodal AIRAG PipelinesFine-Tuning & AlignmentDiffusion ModelsProduction Deployment

12 modules • 200+ hours • 30+ tools • Certificate included

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Core Concept

What Is Generative AI?

Generative AI is a category of artificial intelligence that creates new text, images, code, audio, and video by learning patterns from large datasets. Unlike traditional ML that classifies or predicts, generative models produce original outputs. The core architectures powering this field today are:

01

Large Language Models

LLaMA, GPT, Gemini, DeepSeek

02

Diffusion Models

Stable Diffusion, DALL-E, AnimateDiff

03

Vision-Language Models

CLIP, LLaVA, Qwen-VL, Kosmos-2

04

Code Generation

CodeLLaMA, StarCoder2, DeepSeek-Coder

Your Portfolio

What You’ll Build in This Generative AI Course

Every project in this Generative AI course results in a deployable system you can demo to hiring managers or ship in production. You will build six hands-on projects covering RAG pipelines, fine-tuned LLMs, multimodal applications, containerised APIs, diffusion pipelines, and a full capstone with a live demo URL.

01Retrieval

RAG Pipeline

Hybrid vector + keyword search with re-ranking, source citations, and grounding over your own document corpus.

02Fine-Tuning

Fine-Tuned LLM

Domain-adapted language model using LoRA, evaluated on custom benchmarks with automated quality checks.

03Multimodal

Multimodal App

A working demo that accepts image, text, and speech inputs through a single inference pipeline.

04Deployment

Production API

Model served through vLLM behind FastAPI, containerised with Docker, and deployed on Kubernetes.

05Vision

Diffusion Pipeline

Image generation system with ControlNet guidance, custom LoRA weights, and a ComfyUI workflow.

06Portfolio

Capstone Project

End-to-end GenAI system shipped with a GitHub repo, live demo URL, and an evaluation report.

Career Paths

Who Should Take This Generative AI Course?

This Generative AI course is designed for software engineers, data scientists, ML engineers, and career switchers who want to build, fine-tune, and deploy production-grade AI systems. Each learning path adapts to your current experience level and career goals.

01
Software EngineersGenAI / LLM Engineers

You already write production code. This course adds transformer internals, fine-tuning, and AI system design to your stack.

02
Data ScientistsGenerative AI Specialists

You know ML and stats. Here you go deeper into LLMs, RAG, diffusion models, and production deployment pipelines.

03
ML EngineersLLMOps / AI Platform Leads

You handle model training. This course adds serving, alignment, evaluation, and enterprise-grade GenAI operations.

04
Freshers with PythonJunior GenAI Engineers

You have solid Python. The course builds your math, ML, and deep learning base before any GenAI topics begin.

Before You Start

Prerequisites

No prior deep learning or Generative AI experience is required to enrol. The course builds every concept from the ground up, starting with Python fundamentals and progressing through math, ML, and deep learning before any GenAI topics begin.

Required

  • Working knowledge of Python — functions, loops, OOP basics
  • Comfort with basic math — linear algebra, probability
  • Familiarity with ML concepts — what is a model, training, testing

Helpful but not required

  • Experience with PyTorch or TensorFlow
  • Exposure to NLP or computer vision projects
  • Familiarity with Docker or cloud platforms

The Generative AI learning journey is a structured, engineering-first program covering AI fundamentals, deep learning, transformer architectures, generative models, and production deployment. Over six months of live instructor-led training, you progress from Python and linear algebra through LLMs, RAG, fine-tuning, and serving infrastructure.

Phase 1

Foundations

Build a strong base in Python, linear algebra, probability, and core ML before touching any GenAI topic.

Phase 2

Deep Learning & Transformers

Master neural networks, attention mechanisms, CNNs, RNNs, and the transformer architecture that powers every modern model.

Phase 3

Generative Models

Explore LLMs, vision-language models, diffusion architectures, and multimodal AI systems end to end.

Phase 4

Engineering & Deployment

Apply fine-tuning methods, build retrieval-augmented systems, align models with human preferences, and ship with production-grade serving infrastructure.

By the end, you’ll be ready for roles like GenAI Engineer, LLMOps Specialist, or AI Research Developer — capable of designing systems like RAG-powered assistants, multimodal AI apps, or enterprise copilots from scratch.

The Generative AI course offers three specialized learning tracks based on your background and career goals. The Mastering LLMs track focuses on model training and alignment. The AI Developer track covers app building and integrations. The Data Science plus GenAI track builds foundations for generative AI engineering.

Specialist

Mastering LLMs (LLM-Only Track)

Deep specialization in large language models — training, fine-tuning, alignment, and high-throughput serving.

  • LLaMA 4 / DeepSeek / Gemini; long-context
  • Fine-tuning: LoRA/QLoRA, RLHF, DPO/ORPO
  • Serving: vLLM/TGI, KV cache, speculative decoding
  • Eval & safety: RAGAS, DeepEval, guardrails
Explore LLM Track
Developer

AI Developer (Apps & Integrations)

For devs who want to ship apps — agents, RAG, multimodal UX, and real deployments.

  • LangChain/LlamaIndex, MCP, tool/function calling
  • Hybrid RAG + re-ranking; Pinecone/FAISS/Chroma
  • VLM + Diffusion integrations; streaming UX
  • Deploy: FastAPI, Docker, vLLM/TGI, observability
Explore AI Dev Track
Beginner / Non-ML

Data Science + GenAI Foundations

For freshers or non-ML backgrounds — math, ML, and GenAI fundamentals to get job-ready.

  • Python, Stats, Linear Algebra essentials
  • Classic ML → Transformers basics
  • Your first RAG, LoRA, and evaluation
  • Capstone: end-to-end GenAI project
Explore Foundations Track

Who Is This Generative AI Course For?

This Generative AI course is designed for ML engineers, software developers, and career switchers who want to build production-ready AI systems. Whether you have deep learning experience or are starting with Python fundamentals, the curriculum adapts to your background and builds toward deployment-ready Generative AI skills.

ML / Data Science Engineers

Add GenAI depth to your ML foundation.

  • Fine-tune LLMs with LoRA, QLoRA, RLHF & DPO
  • Build hybrid RAG & multi-vector retrieval pipelines
  • Serve at scale with vLLM, TGI & Triton
  • Evaluate with RAGAS, DeepEval & LangSmith
  • Deploy end-to-end with Docker & Kubernetes
Expected Salary Range
₹22–80 LPA · $140k–$300k

Software Developers

Ship AI-native apps and integrations.

  • LangChain / LlamaIndex agents & tool calling
  • Multimodal UX with VLMs & Diffusion models
  • Streaming chat, function calling & structured outputs
  • Deploy via FastAPI, Docker & scalable APIs
  • Integrate OpenAI, Gemini & Claude APIs
Expected Salary Range
₹15–45 LPA · $120k–$200k

Freshers & Career Switchers

Go from zero to GenAI Engineer.

  • Python → ML → Deep Learning foundations
  • Transformers, attention & positional encoding
  • Build your first RAG pipeline & LoRA fine-tune
  • Evaluation, guardrails & safety from scratch
  • Portfolio projects + mock interviews
Expected Salary Range
₹6–15 LPA · $90k–$140k

Roles You Can Target

Generative AI EngineerLLMOps EngineerRAG Systems ArchitectApplied Research EngineerAI Product EngineerMultimodal EngineerPrompt EngineerAI Solutions Architect
Check Your Eligibility — View Fee Details

Skills You’ll Gain in the Generative AI Course

Skills taught in this Generative AI course are production engineering competencies tested by hiring teams in 2026. They include transformer architecture design, LLM fine-tuning, RAG system building, human preference alignment, multimodal AI development, inference optimization, model evaluation, multi-agent workflows, and enterprise pipeline architecture.

1

Design Transformer Architectures

Understand and build attention mechanisms, positional encoding, and multi-head self-attention from scratch.

2

Fine-Tune Large Language Models

Customize pre-trained LLMs for domain-specific tasks using parameter-efficient methods.

3

Build RAG Systems

Architect retrieval-augmented generation pipelines with chunking, embedding, ranking, and grounding.

4

Align Models with Human Preferences

Apply reinforcement learning and preference optimization to make models safer and more useful.

5

Create Multimodal AI Applications

Combine text, image, video, and speech modalities into unified AI systems.

6

Generate Images & Video with AI

Build and control diffusion-based generation pipelines for creative and enterprise use cases.

7

Deploy Production AI Systems

Containerize, serve, and scale AI models with high-throughput inference infrastructure.

8

Optimize Inference & Serving

Reduce latency and cost with caching, quantization, batching, and parallel decoding strategies.

9

Evaluate & Benchmark AI Models

Measure quality, safety, and reliability using industry-standard evaluation frameworks.

10

Design Multi-Agent Workflows

Orchestrate autonomous AI agents that reason, plan, and use tools across complex tasks.

11

Engineer Prompts for Complex Tasks

Craft structured prompts, few-shot examples, and chain-of-thought patterns for reliable outputs.

12

Architect Enterprise AI Pipelines

Design end-to-end systems with data ingestion, processing, model serving, and monitoring.

Generative AI Tools & Frameworks You’ll Master

The Generative AI tools and frameworks covered in this course are the same ones used in production AI systems at leading companies. You will work hands-on with over 36 tools across training frameworks, orchestration libraries, vector databases, serving infrastructure, API providers, and observability platforms.

PyTorch

Core deep-learning framework for model training and research

PyTorch Lightning

Structured training loops, multi-GPU scaling, and distributed training

Hugging Face Transformers

Pre-trained models, tokenizers, and training pipelines for NLP and vision

Hugging Face Diffusers

Diffusion model pipelines for image, video, and audio generation

PEFT / TRL

Parameter-efficient fine-tuning (LoRA, QLoRA) and RLHF/DPO training

DeepSpeed / FSDP

Distributed training and inference optimization for billion-parameter models

Generative AI Models You’ll Train On

The Generative AI models covered in this course are production-grade architectures you train and fine-tune directly, not just call through APIs. They include large language models like LLaMA and DeepSeek, vision-language models like Qwen-VL, diffusion models like Stable Diffusion XL, and coding models like StarCoder2.

Large Language Models (LLMs)

  • LLaMA 3 / 4 — scaling with large context windows
  • DeepSeek — efficiency + multi-billion parameter MoE
  • Mistral — lightweight high-performance open models
  • Gemini — multimodal reasoning and tool use

From 8B to 400B+ parameters — understand scaling laws, context-window expansion, and what makes each architecture unique.

Vision-Language Models (VLMs)

  • SeamlessM4T — multilingual, multimodal translation
  • Kosmos-2 — grounded multimodal reasoning
  • Qwen-VL — open-source VLM for images and text

Models that reason across images, text, and speech — powering visual QA, document understanding, and cross-modal search.

Diffusion & Generative Media

  • Stable Diffusion XL — advanced text-to-image
  • AnimateDiff — text-to-video and animation
  • Runway Gen-3 / Pika Labs — creative pipelines

Text-to-image, text-to-video, and creative AI — the generative media stack driving modern design and content pipelines.

Coding & Specialized Models

  • CodeLLaMA 70B — code-focused LLM
  • StarCoder2 — structured code generation
  • DeepSeek-Coder — optimized for reasoning in code

Purpose-built for software engineering — code generation, debugging, refactoring, and inline copilot experiences.

How You’ll Customize & Deploy Generative AI Models

Customizing and deploying Generative AI models involves four core technique areas: parameter-efficient fine-tuning with LoRA and QLoRA, alignment using RLHF and DPO, retrieval-augmented generation with hybrid and graph-based RAG patterns, and inference optimization through quantization, KV-cache management, and speculative decoding for production serving.

Parameter-Efficient Fine-Tuning

LoRA · QLoRA · PEFT · DAPT · SFT

What: Inject low-rank adapters into target layers instead of retraining the entire model.

Why: Fine-tune billion-parameter models on consumer GPUs with rapid iteration and minimal compute.

Alignment & Preference Optimization

RLHF (PPO) · DPO · ORPO · RLAIF

What: Steer model behavior toward human-preferred outputs using reward signals and preference pairs.

Why: Safer, more controllable generation — critical for production copilots and enterprise deployment.

Retrieval-Augmented Generation

Hybrid RAG · Graph-RAG · Fusion RAG · Re-ranking

What: Ground LLM responses with external knowledge via chunking, embedding, retrieval, and citation.

Why: Accurate, hallucination-resistant answers for legal, healthcare, and enterprise QA systems.

Serving & Inference Optimization

KV-Cache · Speculative Decoding · MoE Routing · Quantization

What: Maximize throughput and minimize latency with attention-aware caching, draft-model decoding, and expert routing.

Why: Production-grade speed at lower cost — serve thousands of concurrent requests efficiently.

Generative AI Serving & Infrastructure Patterns

High-throughput inference with PagedAttention and continuous batching

Draft-model decoding for 2–3× latency reduction

Mixture-of-Experts routing for compute-efficient scaling

Tracing, evaluation, and observability pipelines for reliability

Containerized deployment with CI/CD and auto-scaling

Edge and on-device deployment for offline and low-latency use

Generative AI Mastery Roadmap

The Generative AI course roadmap is a 12-module, 24-week structured learning path. It begins with Python and math foundations, progresses through neural networks, deep learning, and transformer architectures, then covers LLMs, diffusion models, multimodal AI, fine-tuning, RAG, quantization, RLHF, and agentic AI systems.

Module 1

01.Foundation Refresher

  • Python for AI: NumPy, Pandas, OOP patterns
  • Math: linear algebra, probability, Bayes
  • ML basics: regression, SVM, decision trees
Module 2

02.Neural Network Essentials

  • Perceptrons, activations, back-propagation
  • CNNs (ResNet, EfficientNet), RNNs, LSTMs
  • PyTorch + TensorBoard from scratch
Module 3

03.Applied Deep Learning

  • Vision: YOLOv8, U-Net segmentation
  • NLP: classification, NER, summarization
  • Deploy with ONNX & TorchScript
Module 4

04.Generative AI Fundamentals

  • Autoencoders, VAEs, latent representations
  • GANs: StyleGAN2, CycleGAN, DiffGAN
  • Diffusion: DDPM, ControlNet, ComfyUI
Module 5

05.LLMs Demystified

  • Transformers: attention, multi-head, KV cache
  • GPT / BERT / T5 / LLaMA architecture deep dive
  • Tokenization: BPE, SentencePiece, sampling
Module 6

06.GenAI for Vision

  • VLMs: CLIP, BLIP-2, LLaVA, Gemini
  • Text-to-Image: Stable Diffusion XL, DALL·E 3
  • ViT, DETR, SAM + DragGAN applications
Module 7

07.Multimodal AI Architectures

  • Fusion types: early, cross-attention, late
  • Kosmos-2, GPT-4V, MM-ReAct pipelines
  • Multi-modal agents with LangChain + LLaVA
Module 8

08.Fine-Tuning GenAI Models

  • SFT, LoRA, QLoRA, PEFT techniques
  • Alignment: RLHF (PPO) → DPO → RLAIF
  • Fine-tune LLaMA, Mistral, Phi with Axolotl
Module 9

09.Retrieval-Augmented Generation

  • Chunking, embeddings, hybrid retrieval
  • Vector DBs: FAISS, Qdrant, Pinecone, Weaviate
  • Advanced: Graph-RAG, re-rankers, Haystack
Module 10

10.Quantization & Serving

  • INT8, GPTQ, AWQ, SmoothQuant techniques
  • vLLM, TGI, DeepSpeed-MII inference engines
  • Triton, FastAPI, BentoML serving stacks
Module 11

11.Reasoning & RLHF

  • Chain-of-Thought, ReAct, Toolformer patterns
  • Function calling with LangChain & OpenAI
  • Eval: TruthfulQA, MT-Bench, AlpacaEval
Module 12

12.Agentic AI Introduction

  • Agent types: reflex, goal-based, learning
  • SDKs: AutoGen, CrewAI, LangGraph, SuperAgent
  • Capstone: end-to-end multi-agent project

Generative AI Course Curriculum

The Generative AI course curriculum is a comprehensive, section-by-section breakdown of every topic, tool, and technique covered across 24 weeks of live instructor-led training. Each section includes hands-on modules with real-world projects covering Python, deep learning, transformers, LLMs, diffusion models, RAG, fine-tuning, and deployment.

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Industry-Recognized Generative AI Certificate

Upon completing the Generative AI course, you receive an industry-recognized certificate from the School of Core AI. This certificate validates your expertise in LLMs, VLMs, diffusion models, RAG pipelines, fine-tuning, alignment, and production-grade serving. Each certificate includes a unique verification ID and QR code.

CERTIFICATE

OF ACHIEVEMENT

THIS IS TO CERTIFY THAT

SCHOOL
OF
CORE
AI

SHWETHA SHARMA

Date : 25th Jan 26

Has Successfully Completed The

6-Month Comprehensive Generative AI Training Program

Conducted By The School Of Core AI.

This Intensive Program Included Hands-On Training In Python, Deep Learning, Transformers, LLMs, VLMs, Stable Diffusion, RAG Pipelines, Fine-Tuning (LoRA/QLoRA), RLHF/DPO Alignment, Model Serving (vLLM/TGI), And Agentic AI Fundamentals.

Aishwarya Pandey

Founder and CEO

Certification ID :

SHWETASHARMA250126

SCHOOL
OF
CORE
AI

Each certificate is verifiable with a unique ID and QR code. Share it on LinkedIn, include it in your portfolio, or present it during interviews.

How Our Generative AI Program Outperforms Other Courses

This Generative AI program is built for engineers who want production-level depth, not surface-level overviews. It covers the latest patterns including MCP, hybrid RAG, and production-grade evaluation. The comparison below shows how the curriculum, tools, projects, and career support differ from typical Generative AI courses.

Comparison of our Generative AI program vs other courses
FeatureOur ProgramOther Courses
LLMs & RAG Modules✔ End-to-end RAG: retrieval setup, tool use, function calling, grounding & citations.✘ Prompt-only demos; little grounding or retrieval planning.
MCP (Model Context Protocol) & Tool Interop✔ Portable tool adapters, unified context, and vendor-neutral integration patterns.✘ No MCP; tight coupling to a single SDK/provider.
Hybrid RAG & Re-Ranking✔ BM25 + dense + metadata filters with re-ranking, multi-hop queries, and eval.✘ Single-vector lookups; weak grounding and quality checks.
Project-Based Curriculum✔ 20+ projects: fine-tuning, multimodal apps, domain RAG, internal copilots.✘ 1–2 assignments; no real deployment.
Deployment & Scaling✔ FastAPI, Docker, Kubernetes; vector DBs; CI/CD and autoscale practices.✘ Notebook demos; no infra readiness.
Tooling Ecosystem Mastery✔ Hugging Face, Diffusers, OpenAI SDKs, Pinecone, Chroma, FAISS.✘ Surface-level API coverage.
Evaluation, Tracing & Guardrails✔ RAGAS/DeepEval, LangSmith/LangFuse, policy tests, PII filters, regression suites.✘ Minimal evaluation; no traceability/safety gating.
Live Mentorship & Expert Sessions✔ Weekly live classes, 1:1 mentorship, office hours with AI engineers.✘ Pre-recorded only; no technical mentorship.
Placement Support & Hiring Network✔ Hiring partners, referrals, industry-recognized certificate.✘ Completion certificate only.

Generative AI Course Fees & Enrollment

The Generative AI course fee is a one-time payment of 64,999 INR for six months of live instructor-led training. This all-inclusive fee covers guided projects, a capstone demo, a verifiable certificate, placement assistance, and lifetime access to course materials and community support.

Admissions openNext live batch window: 15th–30thSmall batches

One-time payment

₹64,999

6 months • Live ILT • Capstone

Duration: 6 months
Format: Weekday + Weekend Live ILT
Projects: guided builds + capstone
Certificate: verifiable
Enroll / Get Fee DetailsTalk to our team: +91 96914 40998

We confirm exact batch timings and schedule fit during the call.

₹64,999 includes Live ILT, guided projects, capstone, certificate, and structured support — no hidden charges.

What you'll get

  • Live instructor-led sessions (weekday + weekend options) covering LLMs, VLMs, Diffusion, RAG & fine-tuning.
  • Portfolio-grade builds + 1 capstone demo (ship something real with vLLM/TGI).
  • Code reviews, debugging help, and implementation guidance (not just slides).
  • Interview + portfolio support (resume review, project narration, mock rounds).
  • Recordings + updates access for revisions (so you can catch up anytime).

Best for working professionals: plan for ~8–10 hrs/week (live sessions + build time).

Get the complete enrollment planCall: +91 96914 40998

Generative AI Course fees are 64,999 INR for a 6 month live instructor-led training program with weekday and weekend options, guided projects, capstone demo, and verifiable certificate.

After Generative AI — Advance into LLMOps, MLOps & AIOps

After completing the Generative AI course, the natural career progression is into LLMOps, MLOps, and AIOps specializations. These advanced tracks cover model serving, scaling, evaluation, governance, and enterprise reliability, building directly on the Generative AI foundations you have already mastered.

Deploy & Pipelines

MLOps

Traditional ML pipelines, CI/CD, reproducibility, and reliable deployments.

  • MLflow, TorchServe, Docker, Kubernetes
  • Feature stores, versioning, model registry
  • CI/CD, monitoring, rollback strategies
Explore MLOps
LLM Production

LLMOps

Serve and scale LLMs with evals, tracing, safety, and governance.

  • vLLM/TGI, KV-cache, speculative decoding, MoE
  • Tracing: LangSmith/LangFuse • Evals: RAGAS/DeepEval
  • Guardrails, red-teaming, data governance
Explore LLMOps
Full-Stack Ops

AIOps

Unified operations for ML + LLM + Agent systems at enterprise scale.

  • MLOps + LLMOps + AgentOps integration
  • Cost & latency SLOs, autoscaling, caching
  • Observability & incident response for AI apps
Explore AIOps
Bundle & Save

End-to-End Mastery: GenAI → MLOps → LLMOps → AIOps

Get the full stack with a bundle discount • Limited cohort seats

🚀 Explore All Courses

What Generative AI Learners Say About This Course

Testimonials from Generative AI course graduates reflect real experiences of professionals who transitioned into AI engineering roles. These learners completed the full program including LLMs, fine-tuning, RAG, and deployment, and now work as Gen AI engineers, deep learning engineers, and AI research engineers.

Hear real experiences from professionals who've completed this course

"I was part of the automation team at EY, and wanted to grow beyond rule-based systems. This Generative AI course helped me deeply understand LLMs, fine-tuning, and building agent-based AI systems. From Python fundamentals to deploying RAG pipelines, it covered everything I needed to become a certified Gen AI Engineer."
Aditi Sharma
Gen AI Engineer, EY
"I transitioned from traditional ML to Generative AI, and this course made that shift seamless. The curriculum taught me how to fine-tune models, build with LangChain and LangGraph, and deploy scalable systems using FastAPI and Kubernetes. It’s the most engineering-focused Gen AI course I’ve taken."
Ravi Patel
Deep Learning Engineer, TCS
"Coming from a data analytics background, I was looking to move into core AI engineering. This course helped me master the foundations of transformers, work with vector databases like FAISS and Pinecone, and architect RAG systems for enterprise-scale AI solutions. I now contribute to end-to-end AI systems at work."
Neha Gupta
AI Engineer, Enterprise AI Team
"This course gave me a deep understanding of transformer architectures, attention mechanisms, and diffusion models. We worked on real-world Gen AI systems — not toy projects. I now work on medical document summarization using fine-tuned LLMs and custom embedding pipelines."
Arjun Singh
Machine Learning Engineer, HealthTech
"What stood out in this Generative AI course was the attention to research-backed implementations. We worked with Hugging Face Diffusers, LoRA fine-tuning, and multimodal architectures from scratch. It helped me land a role as an AI research engineer focused on image–text models."
Sanya Mehta
AI Research Engineer, Creative AI Lab
"I joined the course to upskill from ML pipelines to LLM systems. We explored LangGraph, RAG with Pinecone, and LoRA-based fine-tuning of LLaMA. It gave me confidence to architect GenAI stacks and present those to our CTO. It’s deeply technical and thoughtfully structured."
Vikram Rao
Gen AI Engineer, Startup CTO Office

Your Questions Answered – Generative AI Course

Frequently asked questions about the Generative AI course cover enrollment details, prerequisites, course duration, tools and frameworks covered, certification, career support, and payment options. Below are direct answers to the most common questions from prospective students considering this training program.

Got More Questions?

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