Here is the Response, might be helpful for someone 
.
The connectivity among these courses can be structured into a hierarchy from the most foundational concepts (underpinning all modern AI) to the advanced application of those concepts in Agentic AI and Multi-Agent Systems.
The general progression of topics is: Transformer Architecture → Large Language Models (LLM) & Concepts → LLM Applications & Development → Agentic AI & Multi-Agent Systems.
1. Foundational Core (Most Foundational)
These courses cover the core machine learning and model architectures that enable LLMs.
| Course Category | Foundational Course Examples | Description |
|---|---|---|
| Model Architecture | How Transformer LLMs WorkAttention in Transformers: Concepts and Code in PyTorch | Covers the fundamental architecture behind LLMs, including attention mechanisms. |
| Programming/Data | AI Python for Beginners | Provides the necessary programming skills (Python) used by all the frameworks and libraries. |
2. LLM Capabilities and Tuning
These courses focus on the core LLM technology, how to work with it, and how to improve its performance. This knowledge is a prerequisite for building any advanced application, including agents.
| Course Category | Foundational Course Examples | Description |
|---|---|---|
| Model Creation & Tuning | Pretraining LLMsFinetuning Large Language Models | Teaches how to create and fine-tune LLMs from scratch or existing models. |
| Core LLM Application Skills | ChatGPT Prompt Engineering for DevelopersPrompt Engineering with Llama 2&3 | Focuses on effective prompting techniques for LLM interactions and outputs. |
| Efficiency and Deployment (LLMOps) | Quantization Fundamentals with Hugging FaceEfficiently Serving LLMs | Covers optimization for deployment, including quantization and serving strategies. |
3. LLM Application Frameworks and Patterns
These courses focus on the standard methods and frameworks for building LLM-powered applications, especially for data retrieval (RAG). Agents are a special, highly-autonomous type of RAG application.
| Course Category | Foundational Course Examples | Description |
|---|---|---|
| Retrieval-Augmented Generation (RAG) | Building Multimodal Search and RAGJavaScript RAG Web Apps with LlamaIndex | Introduces RAG techniques for enhancing LLMs with external data retrieval. |
| Orchestration Frameworks (Beginner) | LangChain Chat with Your DataBuild LLM Apps with LangChain.js | Teaches beginner-friendly frameworks for orchestrating LLM applications. |
4. Agentic AI Systems (More Foundational)
These courses represent the highest layer, applying all previous knowledge to create autonomous, action-taking systems. The “Agentic AI” course by Andrew Ng is positioned here as a capstone, defining the whole field. Multi-Agent Systems are a sub-set of Agentic AI.
| Course Category | Foundational Course Examples | Description |
|---|---|---|
| Agent Core Concepts | Agentic AIEvaluating AI AgentsBuilding toward Computer Use with Anthropic | Introduces core ideas of agentic systems, evaluation, and tool-using agents. |
| Advanced Agent Frameworks | AI Agents in LangGraphLong-Term Agentic Memory With LangGraphFunctions, Tools and Agents with LangChainBuilding Agentic RAG with LlamaIndex | Covers advanced frameworks for building and managing AI agents. |
| Multi-Agent Systems | Multi AI Agent Systems with crewAIAI Agentic Design Patterns with AutoGenPractical Multi AI Agents and Advanced Use Cases with crewAI | Focuses on systems involving multiple collaborating agents and design patterns. |
In summary, the logical connection from most foundational to most advanced is:
Transformer Architecture → LLM Training/Tuning → Prompt Engineering & Structured Output → RAG & Application Frameworks (LangChain/LlamaIndex) → Agentic AI & Multi-Agent Systems.