🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile
Hands-on Mastery in Generative AI
Don't just learn Generative AI. Build with it. Master modern LLMs, Prompt Engineering, RAG Knowledge Systems, AI Agents, Multi-Agent Workflows, and 5 Real-World Production Projects.
10 Modules • 5 Real-World Projects
10 Modules
AI FearFilter Faculty
What You Will Learn
Understand tokenization, context windows, inference mechanics, and LLM architecture from first principles.
Design few-shot, role-based, and JSON structured output prompts with automated evaluation.
Build and deploy production AI backend APIs integrating vector embeddings, semantic search, and RAG.
Architect autonomous AI Agents equipped with function calling, execution loops, and persistent memory.
Coordinate multi-agent workflows with specialized supervisor and worker agents.
Complete 5 end-to-end production AI projects and prove competency in the Module 10 Skill Challenge.
Curriculum & Weekly Roadmap
10 Structured ModulesModule 1 — Generative AI Fundamentals
- Generative AI
- AI vs ML vs Deep Learning vs Generative AI
- LLM fundamentals
- Tokens
- Context
- Inference
- Real-world applications
Module 2 — Prompt Engineering
- Prompt fundamentals
- Zero-shot prompting
- Few-shot prompting
- Role prompting
- Structured outputs
- Prompt evaluation
- Practical challenges
Module 3 — LLMs & How They Work
- Transformer architecture overview
- Attention mechanism
- Context window
- Embeddings
- Temperature & sampling
- Hallucinations & mitigation
- Model strengths & limitations
Module 4 — AI APIs & Application Building
- Working with AI APIs
- Request & response handling
- System & user messages
- Structured JSON responses
- Error handling & rate limits
- Building your first AI application
Module 5 — RAG & Knowledge Systems
- What is RAG?
- Embeddings explained
- Vector databases
- Document chunking strategies
- Retrieval & similarity search
- Context injection
- Building a RAG application
Module 6 — Building AI Applications
- End-to-end application architecture
- Frontend + Backend + LLM integration
- Input/output flow
- Conversation memory
- Streaming responses
- Deploying an AI app
Module 7 — AI Agents & Tool Calling
- What is an AI Agent?
- Difference between chatbot and agent
- Tools and function calling
- Planning & execution loop
- Agent memory
- Building your first autonomous agent
Module 8 — Multi-Agent Workflows
- Multi-agent architecture
- Agent orchestration
- Specialized agents
- Communication between agents
- Workflow design
- Practical implementation
Module 9 — Real-World GenAI Projects
- Project 1: AI Research Assistant
- Project 2: AI Document Assistant
- Project 3: RAG Knowledge Assistant
- Project 4: AI Customer Support Agent
- Project 5: AI Data Analysis Assistant
Module 10 — Final Hands-on Skill Challenge
- Understand problem
- Design solution
- Build AI application
- Use LLM
- Integrate tools
- Test application
- Submit for review
- AI Agent evaluation
Who This Course Is For
Aspiring AI Engineers, Software Developers, Students, and Technical Creators aiming to build production AI applications and autonomous agents.
Key Skills Developed:
Generative AI FundamentalsPrompt EngineeringLLMs & ArchitectureAI APIs & App BuildingRAG & Knowledge SystemsBuilding AI ApplicationsAI Agents & Tool CallingMulti-Agent WorkflowsReal-World GenAI ProjectsHands-on Skill Challenge
Course Faculty & Development
AI FearFilter Faculty
AI Systems Engineering Team
AI FearFilter Academy
Engineering CurriculumAI FearFilter Academy
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100% Self-Paced + Active Hands-on Learning
Evidence-Based Demonstrated Skill Profile
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