Generative AI & LLMs

Architect production apps with LLMs, RAG & Agents at scale.

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

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

Foundations & Syntax

Understand core concepts, architecture patterns, and algorithmic thinking.

02BUILD

Hands-on Exercises

Construct isolated modules, APIs, pipelines, and component units in live labs.

03CONNECT

Integration & DB

Tie together databases, third-party APIs, authentication, and state stores.

04DEPLOY

Docker & Cloud

Package code into production containers with automated test suites.

05SHIP

Portfolio Capstone

Publish complete codebases with clean Git history for technical interviews.

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

PRODUCTION STANDARDS

Build. Deploy. Prove.

Every project you complete at AIRA follows the exact engineering workflows expected in top tech organizations.

01 / REPOSITORY ARCHITECTURE

Clean Git & Test Suites

Feature branch workflows, automated unit tests with pytest, strict linting, and meaningful commit messages.

02 / REPRODUCIBILITY

Containerized Environments

Multi-stage Dockerfiles and Docker Compose files ensuring zero environment mismatches between local and cloud.

03 / LIVE VERIFICATION

Live Cloud Deployments

Automated continuous deployment to cloud infrastructure providing live demonstration URLs for interviewers.

REAL DELIVERABLES

Capstone projects you will ship

Concrete codebases solving authentic technical 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 technical 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

Generative AI & Prompt Engineering

3 Months structured curriculum with all project labs & mentorship.

₹25,000all 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.