Program Overview

A comprehensive program designed for AI practitioners, developers, and tech leads ready to master
the latest in Generative AI and Large Language Models.

Who Is This For?

  • Data Scientists & ML Engineers
  • AI/ML Practitioners exploring LLMs
  • Developers & Tech Leads
  • Enterprise AI adoption teams

Prerequisites

  • Python programming fundamentals
  • Basic Machine Learning understanding
  • Enthusiasm for cutting-edge AI

Format & Duration

  • 20-25 hours total content
  • Flexible: 5-6 days intensive or spread across weeks
  • Online/Hybrid delivery
  • Hands-on labs & projects

Learning Modules

Module 1: Foundations of LLMs & Generative AI

Learning Outcomes:

  • Understand LLM architecture & ecosystem
  • Explore GPT-4, Gemini, LLaMA, Cohere, Mistral
  • Master prompt engineering methods
  • Zero-shot, few-shot, chain-of-thought techniques

Hands-On Labs

  • Build text-generation app with HuggingFace
  • Experiment with advanced prompting techniques

Module 2: Retrieval-Augmented Generation (RAG) Basics

Learning Outcomes:

  • Master chunking, embeddings & indexing
  • Work with vector databases (Pinecone, Weaviate, FAISS)
  • Build structured & unstructured knowledge bases
  • Enterprise search implementation

Hands-On Labs

  • Create RAG pipeline with PDFs & web data
  • Store embeddings & perform vector retrieval

Module 3: Advanced RAG & Memory Systems

Learning Outcomes:

  • Long-context RAG with memory integration
  • Multi-query retrieval & intelligent routing
  • Advanced chain-of-thought frameworks
  • Decision-making optimization

Hands-On Labs

  • Implement memory-enabled RAG chatbot
  • Build intelligent query routing system

Module 4: Evaluation, Deployment & Optimization

Learning Outcomes:

  • Evaluate RAG performance & reduce hallucinations
  • Monitor with TruLens & Arize
  • Cost optimization strategies
  • Cloud deployment (AWS, GCP, Azure)

Hands-On Labs

  • Deploy RAG to cloud infrastructure
  • Optimize prompts & evaluate performance

Module 5: Fine-Tuning, Agents & Enterprise AI

Learning Outcomes:

  • Fine-tuning vs RAG decision framework
  • Build function-calling agents with LangChain
  • Security, guardrails & compliance
  • Production optimization (latency & throughput)

Hands-On Labs

  • Fine-tune HuggingFace models
  • Implement enterprise workflow agents
  • Apply security guardrails

Perfect for

Data Scientists

Expand your ML expertise into cutting-edge LLMs and RAG systems

AI/ML Engineers

Build production-ready GenAI applications with enterprise focus

Tech Leads

Lead AI transformation initiatives in your organization

Enterprise Teams

Drive organizational AI adoption with hands-on expertise

Program Outcomes

Transform your career with comprehensive GenAI mastery

Master LLMs & RAG Systems

Deep understanding of Large Language Models, prompt engineering, and Retrieval-Augmented Generation architectures

Deploy Scalable AI Pipelines

Practical skills to build and deploy production-ready AI applications on cloud infrastructure

Enterprise-Grade Applications

Build secure, optimized GenAI solutions with proper guardrails and compliance frameworks

Comprehensive Support

Technical Setup

Complete assistance with Python, HuggingFace, LangChain, vector databases, and cloud platforms

Learning Resources

Curated datasets, code notebooks, deployment templates, and comprehensive documentation

Continuous Mentorship

Daily Q&A sessions, troubleshooting labs, and dedicated project support throughout the program

Capstone Project

End-to-end Enterprise RAG-powered AI Assistant – your portfolio-ready GenAI application

NetSkill Enterprise Learning Ecosystem (LMS, LXP, Frontline Training, and Corporate Training) is the state-of-the-art talent upskilling & frontline training solution for SMEs to Fortune 500 companies.

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