Download Brochure

Generative AI Course In Jaipur

Course Feature

Generative AI

Duration 3 Months
Class Timings 1.5 hour a day, 5 days a week
Eligibility

DAAC Artificial Intelligence Course Jaipur

Course Features

Duration: 3 months
Students:Max 10
Skill LeveAdvanced
LanguageEnglish / Hindi
Opening10am to 7pm
ClassesOn System

Generative AI Masterclass — Build LLM Apps, RAG Systems and AI Agents

Become industry-ready with our Generative AI program focused on building production-grade LLM applications, Retrieval-Augmented Generation (RAG) pipelines, fine-tuning strategies, vector databases, and autonomous AI agents using top tools like OpenAI, Google Gemini, LangChain, Hugging Face, and vector stores.

Why this Generative AI Course?

This masterclass is built for 2025 industry needs — focusing on building reliable and scalable generative AI systems (LLM apps, RAG, agents). You'll learn practical patterns used by AI teams in product companies: prompt design, embedding pipelines, LangChain workflows, agent orchestration, and production deployment and monitoring.

Who should enroll?

Software engineers, ML engineers, data scientists, product managers, automation specialists, and technical founders who want to build generative AI products and automation solutions.

Core Tools Covered

OpenAI API (GPT-4.1/5), Google Gemini, Anthropic Claude, LangChain, Hugging Face, Pinecone/Chroma/Weaviate/PGVector, Docker, FastAPI, Python (transformers, sentence-transformers), and GitHub.

Updated Course Syllabus (Modules)

We kept your page structure for site consistency and replaced modules with an industry-standard Generative AI syllabus (Option 1). Each module contains labs and a small project to consolidate learning.

MODULE - 1

Introduction to Generative AI & LLMs

Foundations & Core Concepts

History & evolution of generative models (GPT-4, GPT-5, Gemini, Llama)
Transformers architecture, attention, tokenization, and sequence modeling
Embeddings: what they are and how they power similarity & retrieval
Model types: autoregressive LLMs, encoder-decoder, diffusion & multimodal models
Use-cases: summarization, Q&A, content generation, code assistants, search augmentation

Practical Labs

Calling an LLM API (OpenAI / Gemini) — building your first chat prompt
Experimenting with prompts, system vs user messages, multi-turn context

Minor Lab & Quiz

MODULE - 2

Prompt Engineering

Prompt Design & Advanced Patterns

Basic prompt crafting, constraints, and instruction clarity
Chain-of-Thought prompting and decomposition strategies
Role prompts, templates, and prompt libraries
Function calling & structured outputs (JSON, SQL, CSV)
Prompt-level safety: guardrails, hallucination mitigation, and rate-aware prompting

Practical Labs

Design multi-turn prompts for knowledge-heavy tasks (e.g., product support bot)
Create prompt templates and measure prompt performance

Minor Lab & Project

MODULE - 3

OpenAI, Gemini & Claude — API Development

Working with Provider APIs

OpenAI API fundamentals: chat, completions, function calling, and best practices
Google Gemini API: features, multimodal inputs, and streaming responses
Anthropic Claude basics: safety-first prompts and conversation control
Cost & latency considerations; caching strategies
Secure API usage: keys, rate limits, and monitoring

Practical Labs

Build a small chat application using FastAPI + OpenAI/Gemini backend
Implement function calling and JSON output parsing

Minor Lab & Project

MODULE - 4

LangChain & AI Agents

LangChain Patterns & Agent Design

Introduction to LangChain: chains, tools, memory, and agents
Chains & sequential workflows: LLMChain, SequentialChain, MapReduce
Memory strategies: buffer, summary, and vector memory
Building tools for LLMs (SQL runner, calculators, scrapers)
Creating and orchestrating agents (ReAct, tool-using agents, multi-tool agents)

Practical Labs

Build an agent that answers product & documentation queries using tools + vector DB
Agent testing, sandboxing, and failure modes

Minor Lab & Project

MODULE - 5

Vector Databases & RAG Systems

RAG Architecture & Vector Stores

What is RAG and why it’s crucial for production LLM systems
Document chunking, embeddings, and similarity search
Vector DB options: Pinecone, Chroma, Weaviate, PGVector (pros/cons)
Hybrid search: vector + keyword retrieval & reranking
Context window management, retrieval strategies, and latency trade-offs

Practical Labs

Build a RAG pipeline: ingest docs, embed, store, query, answer
Optimize retrieval prompts and evaluate answer quality

Minor Lab & Project

CAPSTONE

Industry-Grade Projects & Deployment

Capstone Projects (Portfolio Ready)

Project 1 — RAG-Powered Knowledge Assistant (docs + search + chat) using LangChain + Pinecone
Project 2 — AI Agent that automates a business workflow (email summarization + task creation)
Project 3 — Multimodal Q&A (text + images) using Gemini/OpenAI vision models
Project 4 — Deploy LLM App: FastAPI + Docker + CI/CD (basic monitoring & cost controls)
Final demo, code review, documentation & deployment walkthrough

Final Project Assessment & Certificate

Artificial Intelligence Training in Jaipur by DAAC

Why choose DAAC for Generative AI Training In Jaipur

DAAC provides hands-on labs, mentor-led projects, and a job-aligned curriculum focused on building production-ready generative AI systems. Our faculty are industry practitioners who emphasize practical implementation, deployment, and risk-aware AI practices.

Hands-on experience with live projects.
We help you obtain generative AI training, certifications, & jobs in Jaipur.
We offer free demo sessions.
Experienced faculty.
Both practical and theoretical classes are taught.

Artificial Intelligence Classes at DAAC Jaipur

We Will Contact You, At a Time Which Suits You Best

Get In Touch

Benefits of Learning Generative AI Course

Work on high-demand skills: LLMs, RAG, vector DBs, agents.
Build portfolio-ready AI products.
Practical MLOps & deployment experience.
Roles: AI Engineer, Prompt Engineer, RAG Engineer, Agent Builder.
Learn responsible AI & safety best practices.

Professional Artificial Intelligence Course Jaipur DAAC

FAQ

Most Comment Question?

Generative AI uses models (LLMs, diffusion models, multimodal models) to generate text, images, audio, and more. This course is ideal for engineers, data scientists, product builders, automation specialists, and technical managers who want to build production AI applications.

Basic Python knowledge is recommended but we cover practical essentials. The course focuses on applied engineering patterns rather than deep theoretical ML math. Prior programming experience helps but is not strictly required.

You'll work with OpenAI, Google Gemini (APIs), LangChain, Hugging Face, Pinecone/Chroma/Weaviate/PGVector, Docker, FastAPI, and common Python libraries. Optional demo cloud credits or sandbox accounts may be provided for labs.

Yes. The course includes deployment best practices (Docker, FastAPI), cost controls, basic monitoring & logging, and CI/CD for model-backed applications to help you safely ship LLM applications.

Standard program duration is 3 months with flexible batches. The course includes capstone projects, mentor reviews, interview prep, and placement assistance connections.