AI & Generative AI

Architect production apps with LLMs, RAG & Agents at scale. — Gen AI & Prompt Engineering Course in Coimbatore at AIRA Global Tech

Master modern Generative AI engineering — Prompt Design, LangChain, LlamaIndex, Vector Databases (pgvector/Pinecone), Retrieval-Augmented Generation (RAG), and Autonomous Multi-Agent Workflows.

Industry Software & Practical Skills CoveredHands-on Labs
LangChainLlamaIndexOpenAIpgvectorOllama
Duration3 Months
FormatLive Labs & Projects
LanguagesEnglish, Tamil, Hindi
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Get Course Syllabus & Fee Details

THE METHODOLOGY

How You'll Progress Through This Program

01LEARN

Generative AI Foundations

LLMs, tokens, context, multimodal AI and modern generative AI concepts.

02PROMPT

Prompt Engineering

Structured prompts, system instructions, role prompting and reliable outputs.

03WORKFLOW

AI Workflows

Build reusable AI workflows using APIs, structured outputs and automation.

04AGENTS

RAG & AI Agents

Retrieval, knowledge bases, tool calling and agentic workflows.

05SHIP

AI Portfolio

Build practical GenAI applications, assistants and automation projects.

IN-DEPTH ROADMAP

A curriculum built around real engineering

MODULE 01

LLM Fundamentals & Advanced Prompt Engineering

3 Weeks

Transformer architecture intuition, tokenization, temperature controls, few-shot prompt design, and structured JSON outputs.

What You Will Learn

  • How LLMs work: self-attention, tokens, embeddings, and context windows
  • System prompting, zero-shot, few-shot, and Chain-of-Thought (CoT) prompting
  • Constrained generation, JSON mode, and function calling with tool use
  • Running local open-source LLMs (Llama 3, Mistral) via Ollama and vLLM
  • Prompt evaluation frameworks, latency optimization, and cost modeling
Hands-on Lab Deliverable

Build an automated contract analysis prompt suite with strict JSON validation and hallucination checks.

THE TOOLCHAIN

How the engineering stack connects

You won't learn isolated tools. You'll understand how databases, APIs, client interfaces, and cloud deployments operate as a unified system.

Agentic Framework

LangChain & LangGraph

Core framework for building complex LLM chains and cyclical multi-agent systems

Data Ingestion

LlamaIndex

Data framework for structured document parsing and advanced RAG pipelines

Foundation Models

OpenAI / Claude API

Frontier LLM APIs with function calling and vision capabilities

Local LLMs

Ollama / vLLM

High-throughput inference engine for running open-source models locally

Vector Database

pgvector

PostgreSQL extension for high-dimensional vector similarity indexing

Vector Storage

ChromaDB

Open-source embedding database for rapid local prototyping

Backend Engine

FastAPI

Streaming API endpoints for real-time token delivery to clients

LLM Evaluation

Ragas

Automated evaluation framework assessing RAG faithfulness and precision

AI DEVELOPMENT STANDARDS

Prompt. Integrate. Automate.

Architect deterministic, reliable, and production-ready generative AI systems and agentic workflows.

01 / PROMPT QUALITY

Structured & Reliable Prompts

Few-shot prompting, JSON schema enforcement, system instructions, and hallucination reduction workflows.

02 / AI WORKFLOWS

APIs, RAG & Tool Integration

Vector database retrieval, semantic embeddings, function calling, and context window optimization.

03 / AI APPLICATIONS

Practical AI Assistants & Agents

Multi-step autonomous agent systems, conversational interfaces, and intelligent business process automations.

REAL DELIVERABLES

Capstone projects you will ship

Concrete projects solving authentic industry requirements.

PROJECT 01Enterprise AI

Enterprise Multi-Document RAG System

Production RAG application answering queries across thousands of financial PDF documents with verified page citations.

PythonLangChainpgvectorFastAPIOpenAIRagas
Architecture Deliverable

Hybrid retrieval combining BM25 keyword matching with dense Cohere re-ranking.

PROJECT 02Autonomous Systems

Autonomous Code Refactoring Agent

Multi-agent LangGraph system that analyzes GitHub pull requests, identifies security vulnerabilities, and generates tests.

LangGraphPythonGitHub APILlama 3PyTest
Architecture Deliverable

Hierarchical supervisor agent routing code to specialized security and linting sub-agents.

PROJECT 03Edge & Private AI

Local Private LLM Knowledge Copilot

Completely offline, air-gapped AI assistant running open-source LLMs on local hardware for data-privacy compliance.

OllamaMistral 7BChromaDBFastAPIReact
Architecture Deliverable

Quantized 4-bit model inference pipeline running efficiently on consumer GPUs.

CAREER DIRECTIONS

Target industry roles

Generative AI Engineer

Designs and builds LLM-powered applications, RAG pipelines, and autonomous agent systems.

AI Application Developer

Integrates frontier AI APIs into full-stack production software and enterprise products.

Prompt Engineer & LLM Specialist

Develops high-precision prompt architectures, evaluations, and structured tool-calling flows.

PROGRAM ADMISSION

All-Inclusive Program Fee

Complete industry curriculum, live 1:1 mentorship, capstone project evaluations, and placement support.

All-Inclusive Access
COMPLETE PROGRAM

Gen AI & Prompt Engineering

3 Months structured curriculum with all project labs & mentorship.

₹40,000all modules & mentorship included
All modules & lab exercises
Weekly 1:1 mentorship & reviews
Capstone project evaluations
Resume & technical mock drills
Direct referrals to hiring partners
Certificate of Completion
QUESTIONS & ANSWERS

Frequently Asked Questions

Basic Python programming knowledge is required. You do not need deep mathematical ML knowledge, as this course focuses on engineering with foundation models, RAG pipelines, and agent frameworks.