https://github.com/msg-careerpaths/node-ai-training

This is the official Node AI Training for .msg Career Paths
https://github.com/msg-careerpaths/node-ai-training

angular aws aws-bedrock langchain-js nestjs prompt-engineering rag typescript vector-database

Last synced: 2 months ago
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This is the official Node AI Training for .msg Career Paths

• Host: GitHub
• URL: https://github.com/msg-careerpaths/node-ai-training
• Owner: msg-CareerPaths
• License: gpl-3.0
• Created: 2025-11-16T21:36:43.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-11-17T08:35:19.000Z (7 months ago)
• Last Synced: 2025-11-17T10:21:20.786Z (7 months ago)
• Topics: angular, aws, aws-bedrock, langchain-js, nestjs, prompt-engineering, rag, typescript, vector-database
• Language: TypeScript
• Homepage:
• Size: 466 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Node AI Training

>

> Note this training assumes you have some previous experience with `Angular`, `Nest.js` and `AWS`.

>

> If you do not, please consult those training paths beforehand.

>

## Mandatory Content

- Introduction

  - [0.0 Introduction](./chapters/000-introduction/000-introduction.md)

  - [0.1 Foundational Theory](./chapters/000-introduction/001-foundational-theory.md)

- Prompt Engineering and Integration

  - [1.0 Introduction](./chapters/100-prompt/100-prompt-intro.md)

  - [1.1 Prompt Integration](./chapters/100-prompt/101-prompt-integration.md)

  - [1.2 Prompt Strategies](./chapters/100-prompt/102-prompt-strategies.md)

  - [1.3 Prompt Context](./chapters/100-prompt/103-prompt-context.md)

  - [1.4 Prompt Tooling](./chapters/100-prompt/104-prompt-tooling.md)

  - [1.5 Prompt Evaluations](./chapters/100-prompt/105-prompt-evaluations.md)

- Chat and Streaming

  - [2.0 Chat LLM Streaming](chapters/200-chat/200-chat-llm-streaming.md)

- Retrieval Augmented Generation (RAG)

  - [3.0 RAG Intro](./chapters/300-rag/300-rag-intro.md) 

  - [3.1 RAG Theory Vectors and Embeddings](./chapters/300-rag/301-rag-theory-vectors-and-embeddings.md) 

  - [3.2 RAG Document Vectors and Parsing](./chapters/300-rag/302-rag-document-vectors-and-parsing.md) 

  - [3.3 RAG Structured Extraction](./chapters/300-rag/303-rag-structured-extraction.md)

- Agent Orchestration

  - [4.0 Agent Orchestration Intro](./chapters/400-agent-orchestration/400-agent-orchestration-intro.md)

- Model Context Protocol (MCP)

  - [5.0 MCP Intro](./chapters/500-mcp/500-mcp-intro.md)

## Training App

The training provides a sample application you will use to integrate LLM functionalities into it. It can be found under the [app](./app) folder.