From Generative AI Foundations to Production-Grade Multi-Agent Systems By SkillSet Arena
Duration: 35-38 Hours
Level: Beginner – Advanced – Enterprise-Ready
Mode: Live + Hands-on + Capstone
Outcome: Build, Deploy & Scale AI Agents for Real Business Use Cases
Program Vision:
This program is designed to transform learners from AI users – AI builders – AI system architects by enabling them to design, deploy, and scale real-world Agentic AI systems.
HOW SKILLSET ARENA WILL HELP
Hands-on Industry Learning:
- Live coding sessions with real-world datasets
- 10+ practical projects (not just theory)
- Industry use-case driven curriculum
Mentorship & Expert Guidance:
- Learn from AI/ML industry experts
- Weekly doubt-solving sessions
- Personalized project feedback
Career & Placement Support:
- Resume building (AI-focused)
- Mock interviews (AI/LLM roles)
- Portfolio development with GitHub projects
- Job & placement assistance
Enterprise-Level Exposure:
- Work on real business problems
- Learn tools used by top companies
- Build deployable AI systems
Certification & Industry Recognition:
- SkillSet Arena Certification
- Capstone project showcase
- LinkedIn portfolio branding
Why This Program?
Generative AI is no longer about chatbots.
Organizations are now building:
- Autonomous AI Agents
- Multi-Agent Workflows
- Enterprise RAG Systems
- AI Research Assistants
- AI Business Analysts
This program trains participants to design and deploy production-grade AI systems using:
- LangChain
- LangGraph
- CrewAI
- RAG Architectures
- MCP (Model Context Protocol)
- FastAPI Deployment
PROGRAM MODULES
MODULE 1: Foundations of Generative AI
What You Learn:
- AI vs ML vs Deep Learning vs Generative AI
- Evolution of LLMs
- Transformer architecture (intuitive)
- Attention mechanism
- Foundation models ecosystem
Learning Outcomes:
- Understand how modern LLMs work
- Explain Generative AI architecture confidently
- Identify enterprise GenAI use cases
- Build conceptual clarity before coding
MODULE 2: LLM Architecture & Engineering
What You Learn:
- Transformer deep dive
- Tokenization & embeddings
- Pre-training, fine-tuning, RLHF
- Cost & token optimization
Learning Outcomes:
- Estimate token usage and cost
- Optimize prompts for performance
- Understand model limitations
- Design efficient LLM-powered systems
MODULE 3: Python for LLM Development
What You Learn:
- API integration
- JSON & HTTP handling
- Environment variables
- Error handling
- First LLM application
Learning Outcomes:
- Connect securely to LLM APIs
- Build your first AI chatbot
- Handle responses & errors robustly
- Prepare foundation for production apps
MODULE 4: NLP Foundations (Hands-On)
What You Learn:
- Text preprocessing
- Tokenization
- Lemmatization & stemming
- TF-IDF & N-grams
Learning Outcomes:
- Preprocess enterprise text data
- Build basic text analyzers
- Understand text representation methods
- Prepare data for RAG systems
MODULE 5: Prompt Engineering & Context Engineering
What You Learn:
- Zero-shot & few-shot prompting
- Chain-of-thought reasoning
- Structured JSON output prompts
- Guardrails & hallucination control
- Prompt templates & dynamic prompts
Learning Outcomes:
- Design production-grade prompts
- Reduce hallucinations
- Generate structured outputs
- Build intelligent AI assistants
Hands-on:
- AI Email Assistant
- Resume Parser
- Case Study Solver
MODULE 6: LLM Application Development
What You Learn:
- Chat memory
- Tool calling
- Function calling
- Streaming responses
- System design basics
Learning Outcomes:
- Build conversational AI apps
- Integrate tools with LLMs
- Design scalable LLM workflows
Mini Projects:
- AI Study Assistant
- AI Business Analyst Bot
MODULE 7: RAG (Retrieval Augmented Generation)
What You Learn:
- RAG architecture
- Chunking strategies
- Embeddings
- Vector databases (FAISS, ChromaDB)
- Retrieval & re-ranking
Learning Outcomes:
- Build enterprise document Q&A systems
- Reduce hallucination in AI systems
- Integrate private knowledge bases
- Design scalable RAG pipelines
Hands-on:
- PDF Question Answering System
- Company Policy Chatbot
MODULE 8: Agentic AI Foundations
What You Learn:
- Chatbot vs AI Agent
- ReAct pattern
- Plan-Execute model
- Multi-agent systems
- Reflection loops
Learning Outcomes:
- Design autonomous agents
- Implement planning & reasoning
- Build tool-using AI agents
- Architect agent workflows
Hands-on:
- Research Agent
- Data Analyst Agent
MODULE 9: LangGraph – Stateful Agent Orchestration (Using LangGraph)
What You Learn:
- Graph-based workflows
- State management
- Conditional routing
- Loop-based reasoning
- Supervisor-worker systems
Learning Outcomes:
- Build stateful AI agents
- Design complex multi-step workflows
- Implement reflection loops
- Create production-grade agent graphs
Hands-on:
- Multi-step Decision Agent
- Supervisor & Worker Graph System
MODULE 10: CrewAI – Multi-Agent Collaboration (Using CrewAI)
What You Learn:
- Role-based agent design
- Task delegation
- Hierarchical vs sequential processes
- Autonomous collaboration
Learning Outcomes:
- Design collaborative AI teams
- Build research & strategy agent systems
- Implement task-based agent workflows
- Develop enterprise-level multi-agent automation
Hands-on:
- Research Crew (Researcher + Writer + Editor)
- HR Screening Crew
MODULE 11: MCP – Model Context Protocol
What You Learn:
- Structured tool communication
- Tool server architecture
- Enterprise integration
- MCP vs traditional APIs
Learning Outcomes:
- Architect scalable AI ecosystems
- Standardize tool communication
- Build enterprise-grade AI systems
MODULE 12: Agents to Production
What You Learn:
- Cost optimization
- Monitoring & logging
- Prompt injection defense
- FastAPI deployment
- Docker basics
- Async execution
Learning Outcomes:
- Deploy AI systems confidently
- Monitor and control costs
- Secure LLM applications
- Scale AI agents in production
CAPSTONE PROJECTS
Participants will build:
- Enterprise RAG + Agent System
- Multi-Agent Research Assistant
- AI HR Resume Screening System
- Financial Analyst Agent
- AI Business Strategy Planner
Why Companies Choose SkillSet Arena:
- Train teams to build in-house AI systems
- Reduce dependency on external vendors
- Automate workflows using AI agents
- Enable AI-driven decision making
- Build scalable AI infrastructure
Target Audience:
- Software Developers
- Data Scientists / Analysts
- AI/ML Engineers
- Product Managers
- Innovation Teams
- Entrepreneurs & Founders
This program is not just about learning AI:
- It’s about building real AI systems that work in the real world.
- It’s about moving from ChatGPT users – AI creators – AI leaders.
- And positioning yourself at the forefront of the Agentic AI revolution.