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Master In Generative AI Training
4.5/5

Level

Advanced

Duration

8 weeks

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What is Master In Generative AI Training?

The Master in Generative AI Training program is an advanced, end-to-end curriculum designed to build strong conceptual and practical expertise in modern Generative AI systems. Delivered through Jast Tech, this course takes learners from core AI and deep learning principles to cutting-edge Large Language Models, diffusion models, and multimodal AI applications. Participants gain hands-on experience with prompt engineering, vector databases, retrieval-augmented generation (RAG), fine-tuning LLMs, and deploying scalable AI solutions. The program emphasizes real-world implementation using industry tools such as OpenAI APIs, Hugging Face, LangChain, and cloud platforms. Learners explore responsible AI practices, security considerations, and performance optimization techniques required in enterprise environments. By integrating theory with applied projects, this course prepares professionals for roles such as Generative AI Engineer, AI Solution Architect, and Applied LLM Specialist. The curriculum is structured to match current industry demands, ensuring learners can confidently design, build, and deploy production-ready Generative AI systems across domains like healthcare, finance, education, and content automation.

Job Roles You Can Achieve

After completing this course

  • Solutions Architect
  • Technical Consultant
  • Implementation Specialist
  • System Administrator
  • IT Professional

Master In Generative AI Training Curriculum

1
Module 01

Foundations of AI & Generative Models

Establishes the mathematical and conceptual base needed to understand how generative systems learn data distributions and create new content.

Introduction to AI, ML, and Deep Learning
Evolution of Generative AI
Discriminative vs Generative Models
2
Module 02

Neural Networks & Transformers

Explains why transformers power modern LLMs and how attention enables contextual understanding at scale.

Deep neural networks refresher
Attention mechanisms
Transformer architecture deep dive
3
Module 03

Large Language Models (LLMs)

Focuses on how LLMs are built, optimized, and used in production systems.

GPT, BERT, T5 architectures
Tokenization and embeddings
Training vs inference workflows
4
Module 04

Prompt Engineering & Optimization

Teaches systematic methods to control LLM outputs for accuracy, reasoning, and consistency.

Prompt design patterns
Chain-of-thought prompting
Prompt evaluation and tuning
5
Module 05

Embeddings & Vector Databases

Covers the backbone of semantic search and contextual retrieval in AI applications.

Semantic embeddings
Vector similarity search
FAISS, Pinecone, ChromaDB

Related Courses

Training Roadmap

Seven intentional milestones — from first session to dream job.

Onboarding

01
  • Meet your industry mentor
  • Define your goals
  • Skill gap assessment

Core Learning

02
  • Live interactive classes
  • AI-curated content
  • Recorded sessions

Hands-on Practice

03
  • Weekly assignments
  • MCQ evaluations
  • Module quizzes

Real Projects

04
  • 3 live industry projects
  • Portfolio building
  • Case studies

Mentorship

05
  • 1:1 doubt sessions
  • Peer collaboration
  • Expert feedback

Certification

06
  • Exam preparation
  • Practice dumps
  • Industry-recognised certificate

Career Launch

07
  • Resume crafting
  • Mock interviews
  • Job placement support

Key Projects

Hands-on experience with real-world scenarios designed for mastery.

Enterprise Knowledge Assistant using RAG

This project involves building a Generative AI-powered enterprise assistant that answers user queries using internal documents by combining embeddings, vector databases, and retrieval-augmented generation. Learners design document ingestion pipelines, implement semantic search, and integrate an LLM to generate accurate, context-aware responses while minimizing hallucinations, simulating real corporate knowledge systems.

Custom Fine-Tuned LLM for Domain-Specific Q&A

In this project, learners fine-tune a pre-trained large language model on domain-specific datasets such as legal, healthcare, or finance content. The focus is on data preparation, parameter-efficient fine-tuning techniques, evaluation metrics, and deploying the customized model as an API for real-world usage.

Multimodal Content Generation Platform

This project focuses on developing a platform that generates text, images, and summaries using multimodal Generative AI models. Learners integrate text-to-image models with LLMs, design user workflows, and deploy a scalable system capable of handling creative and business-driven content generation tasks.

Available Course Schedules

Select a schedule that works best for you

Weekend

Starts

23 May 2026

Time

09:30 AM – 12:30 PM

Duration

8 weeks

Weekdays

Starts

25 May 2026

Time

07:00 AM – 09:00 AM

Duration

8 weeks

Weekend

Starts

30 May 2026

Time

02:00 PM – 05:00 PM

Duration

8 weeks

Weekdays

Starts

01 Jun 2026

Time

08:00 PM – 10:00 PM

Duration

8 weeks

Need a custom schedule?

Our team will craft the perfect batch for you.

What Our Happy Clients Say

Real Feedback from our clients

What We Offer Beyond Courses

24/7 Support

Round-the-clock assistance

LinkedIn Profile

Professional profile building

Resume Writing

Expert resume crafting

Alumni Guidance

Mentorship from graduates

Interview Prep

Mock interviews & tips

Live Projects

Real-world experience

Review from Tejas Kumar

Tejas Kumar

Review from Sakshi Singh

Sakshi Singh

Review from Sanjay Patel

Sanjay Patel

Specialized Training Programs

JastTech For Corporates

JastTech Courses

Certification Details

Master In Generative AI Training – Associate

  • Exam Name

    Master In Generative AI Training – Associate

  • Exam Code

    SAA-C03

  • Duration

    130 minutes

  • Format

    Multiple Choice & Multi-Response

  • Passing Score

    720 (Scale: 100–1000)

  • Level

    Associate

Certificate of Completion

Prepare

Top Interview Questions

Curated questions with expert answers to help you ace your next interview.

Q1: What is Generative AI?

Generative AI refers to models that learn data distributions and generate new content such as text, images, audio, or code.

Q2: What problem does RAG solve?

RAG reduces hallucinations by grounding model responses in external, retrievable knowledge sources.

Q3: Difference between fine-tuning and prompt engineering?

Prompt engineering modifies inputs to guide outputs, while fine-tuning updates model weights for deeper adaptation.

Q4: Why are transformers important in LLMs?

Transformers handle long-range dependencies efficiently using attention mechanisms, enabling contextual understanding.

Q5: What are embeddings used for?

Embeddings convert data into vector representations that capture semantic meaning for search and retrieval tasks.

Support

Frequently Asked FAQs

Can't find what you're looking for? Reach out to our support team anytime.

Q1: Who should take this course?

This course is ideal for software engineers, data scientists, ML engineers, and tech professionals aiming to specialize in Generative AI.

Q2: Are coding skills required?

Yes, basic Python knowledge is required. Advanced AI concepts are taught from the ground up.

Q3: Does the course cover real-world tools?

Yes, it includes OpenAI APIs, Hugging Face, LangChain, vector databases, and cloud deployment practices.

Q4: Is this course suitable for enterprise roles?

Absolutely. The curriculum is aligned with enterprise AI architecture and deployment standards.

Q5: Will I build hands-on projects?

Yes, multiple production-style projects are included to reinforce practical skills.

The support team was very cooperative and responsive. They made sure all doubts were cleared without delay. Great experience overall.

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One thing I really liked about the Data Analyst course at JastTech is their focus on consistency. Regular sessions and tasks help you stay on track and build a daily learning habit. Also, they provide recordings after live sessions, which help in revision.

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Sanmitra Kamble

I joined JastTech for the DFT course a few months back. At first, I wasn’t sure what to expect, but the classes turned out to be really helpful. The teaching is simple and not too complicated, which helped me keep up.

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sachin kumar

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