MENMOZHI TECHNOLOGIES PRIVATE LIMITED 🎁 COMPLIMENTARY INDUSTRY INTERNSHIP INCLUDED

Generative AI for Developers

A Comprehensive, Production-Oriented Engineering Curriculum

50 Hours Intense Live Training
14 Modules Production Frameworks
Live Projects Internship Experience

Exclusive Program Benefit: Industry Internship

Every enrolled participant receives an exclusive, practical Complimentary Internship Framework managed by Menmozhi Technologies Private Limited.

Transition directly from structured instruction to live implementation. Work remotely or hybrid on production pipelines, deploy models to cloud platforms, and receive an official Certificate of Internship alongside your course completion credentials to reinforce your portfolio.

Who Can Enroll?

Engineered for ambitious professionals, developers, and innovators looking to lead the next generation of AI software architecture.

Course Curriculum

Module 01

Environment Setup & Developer Tools

2 Hours
  • Python
  • VS Code
  • Git & GitHub
  • OpenAI/Gemini SDKs
  • Ollama
  • Docker
Outcome: Build, containerize, and isolate your complete, professional localized/cloud GenAI dev ecosystem.
Module 02

Working with LLM APIs

4 Hours
  • Text Generation
  • Summarization
  • Code/SQL Compiling
  • Structured JSON Outputs
Outcome: Programmatically interface with enterprise and local models with consistent, deterministic functional parsing.
Module 03

Advanced Prompt Engineering

3 Hours
  • Zero-Shot
  • Few-Shot Learning
  • Role Prompting Techniques
  • Dynamic Prompt Templates
Outcome: Architect reliable contextual guardrails and prompt components that maximize backend LLM reasoning efficiency.
Module 04

Building Conversational Chatbots

4 Hours
  • Chat Interfaces
  • Memory Management
  • Context Persistence
  • Session Authentication
Outcome: Implement production-ready conversational agents featuring persistent state tracking across user sessions.
Module 05

Embeddings & Semantic Search

3 Hours
  • Vector Encodings
  • Vector Spaces
  • Similarity Search Mechanics
  • FAISS
  • ChromaDB
Outcome: Translate complex, unstructured text data into multi-dimensional vectors for fast similarity indexing.
Module 06

Retrieval-Augmented Generation (RAG)

6 Hours
  • PDF Extraction Pipeline
  • Chunking Strategies
  • Vector Retrieval
  • Contextual Injection
Outcome: Mitigate model hallucinations by anchoring generation routines directly onto structured and unstructured file sources.
Module 07

Multi-Document Knowledge Systems

3 Hours
  • Concurrent PDF Ingestion
  • Metadata Filtering
  • Strict Source Attribution
Outcome: Orchestrate automated multi-document corpus ingest systems capable of high-fidelity citation tracking.
Module 08

LangChain Application Development

4 Hours
  • Sequential Chains
  • Memory Subsystems
  • Custom Tool Composition
  • Output Parsers
Outcome: Build modular, flexible, and deeply integrated software pipelines leveraging the LangChain software suite.
Module 09

Autonomous AI Agents

5 Hours
  • Dynamic Tool Calling
  • Native Function Invocation
  • LangGraph Graphs
  • CrewAI
Outcome: Construct multi-agent collaborative graphs capable of independent loop execution, objective decomposing, and self-correction.
Module 10

Voice AI Applications

3 Hours
  • Speech-to-Text (STT)
  • Text-to-Speech (TTS)
  • Real-time Latency Optimizations
Outcome: Develop human-grade, interactive acoustic loops providing low-latency verbal response interaction.
Module 11

Image & Vision AI Applications

3 Hours
  • Text-to-Image Generation
  • Multimodal Vision Parsing
  • Intelligent OCR
Outcome: Build end-to-end multimodal logic loops capable of translating cross-format contextual imagery datasets.
Module 12

Fine-Tuning Open Source Models

4 Hours
  • LoRA Implementations
  • QLoRA Quantization
  • Hyperparameter Selection
  • Loss Evaluation
Outcome: Adapt base weights of deep models to hyper-specialized domains using consumer/mid-tier cloud hardware allocations.
Module 13

AI Web Application Development

4 Hours
  • Flask Web Framework
  • FastAPI Async Engines
  • Streamlit UIs
  • DB Ingestion Layers
Outcome: Wrap backend script routines inside robust REST API microservices, exposed through reactive UI instances.
Module 14

Production Deployment & Monitoring

2 Hours
  • Containerization Strategy
  • AWS Stack Deploy
  • Azure Cloud Architecture
  • Telemetry Systems
Outcome: Ship, scale, and monitor distributed network clusters under active traffic patterns with telemetry tracking.

Production Tech Stack Covered

Programming
Python, SQL
LLM Systems
GPT-4, Gemini Pro, Claude, Llama 3, Mistral
Frameworks
LangChain, LangGraph, CrewAI, LlamaIndex
Vector Infrastructure
ChromaDB, FAISS
Web Delivery
Flask, FastAPI, Streamlit
Cloud Architecture
AWS, Azure, Google Cloud Engine