Welcome to Generative AI & Agentic AI by CodeSanskriti. This premium course is designed for developers, architects, and tech professionals who want to build intelligent, autonomous, and production-grade AI systems.
You will learn how modern AI models work, how to design Generative AI applications, and how to build Agentic AI systems that can think, plan, act, and collaborate — exactly how real-world AI products are built today.
Jump to: AI Foundations • NLP & Transformers • Generative AI • LLMs • Prompt Engineering • LLM APIs • Embeddings • RAG • Agentic AI • Agent Architecture • Agent Types • Tools & Planning • Memory • Multi-Agent • Safety • Use Cases • Capstone
🧠 Section 1 — AI Foundations
Build a strong conceptual base for Generative and Agentic AI.
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Lesson 1 — Introduction to AI, ML & Deep Learning
Understand the evolution of Artificial Intelligence.
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Lesson 2 — Supervised, Unsupervised & Reinforcement Learning
Core learning paradigms explained simply.
🗣️ Section 2 — NLP & Transformer Fundamentals
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Lesson 3 — Natural Language Processing Basics
How machines understand human language.
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Lesson 4 — Transformers & Attention Mechanism
Why transformers power modern AI systems.
✨ Section 3 — Introduction to Generative AI
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Lesson 5 — What is Generative AI?
Text, image, audio, and video generation explained.
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Lesson 6 — Limitations, Hallucinations & Risks
Understanding where GenAI can fail.
🤖 Section 4 — Large Language Models (LLMs)
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Lesson 7 — How Large Language Models Work
Tokens, context window, training & inference.
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Lesson 8 — Open-Source vs Closed-Source LLMs
Choosing the right model for your use case.
🧩 Section 5 — Prompt Engineering
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Lesson 9 — Prompt Engineering Fundamentals
How to communicate effectively with AI.
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Lesson 10 — Advanced Prompting Techniques
Few-shot, chain-of-thought, and role prompting.
🛠️ Section 6 — Working with LLM APIs
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Lesson 11 — Using LLM APIs
Parameters, streaming, and cost optimization.
📦 Section 7 — Embeddings & Vector Databases
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Lesson 12 — Embeddings & Semantic Search
How AI understands meaning.
🔍 Section 8 — Retrieval Augmented Generation (RAG)
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Lesson 13 — RAG Architecture & Pipelines
Enhancing AI with your own data.
🤖 Section 9 — Introduction to Agentic AI
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Lesson 14 — What is Agentic AI?
From passive responses to autonomous action.
🔄 Section 10 — Agent Architecture
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Lesson 15 — Think, Plan, Act, Observe Loop
How agents make decisions.
🧠 Section 11 — Types of AI Agents
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Lesson 16 — Reactive, Goal-Based & Planning Agents
Different agent behaviors explained.
🧰 Section 12 — Tools, Planning & Reasoning
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Lesson 17 — Tools & Function Calling
Letting AI take real actions.
💾 Section 13 — Memory in Agentic AI
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Lesson 18 — Short-Term & Long-Term Memory
Persistent context for agents.
👥 Section 14 — Multi-Agent Systems
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Lesson 19 — Multi-Agent Collaboration
AI agents working as teams.
🔐 Section 15 — AI Safety & Governance
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Lesson 20 — Safe, Responsible & Secure AI
Guardrails, bias, and compliance.
🚀 Section 16 — Real-World Applications
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Lesson 21 — Industry Use Cases
AI copilots, automation, and assistants.
🎓 Section 17 — Capstone Projects
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Capstone — Build an Agentic AI System
Design and build a real-world AI product.