AI Engineering Lab

Personal AI engineering learning lab.

This repository organizes a 2026 learning path toward practical AI engineering, with a strong focus on:

• local LLMs;
• workflow automation;
• RAG;
• agents;
• business watch systems;
• multimodal AI;
• robotics;
• deployable AI platforms.

Updated project sequence

Order	Project	Focus
01	2026_project_01--llm_playground	LLM sampling, inference parameters, basic experiments
02	2026_project_02--local_llm_server	Local LLM API server, OpenAI-compatible patterns
03	2026_project_03--workflow_automation_lab	n8n, JS Code Nodes, ingestion, parsing, normalization, scoring, alerts
04	2026_project_04--rag_system	Basic Retrieval Augmented Generation
05	2026_project_05--rag_enterprise	Production-grade RAG patterns
06	2026_project_06--ai_agents_lab	Agents, tools, state, human-in-the-loop
07	2026_project_07--business_watch_agent	Watch systems combining workflows, memory, retrieval and agents
08	2026_project_08--vision_ai_lab	Computer vision foundations
09	2026_project_09--multimodal_assistant	Multimodal assistant experiments
10	2026_project_10--robot_ai_system	Robotics AI experiments
11	2026_project_11--ai_platform	Integrated local AI platform

Learning logic

LLM foundations
  ↓
workflow automation
  ↓
RAG systems
  ↓
agents
  ↓
business watch
  ↓
vision / multimodal / robotics
  ↓
AI platform

Why workflow automation was added before RAG

RAG and agents require solid data-pipeline thinking.

Before building retrieval systems, it is useful to learn how to:

• fetch data;
• parse sources;
• normalize records;
• deduplicate;
• score;
• store;
• alert;
• manage uncertainty.

This is the role of 2026_project_03--workflow_automation_lab.

Its first use case is BD Kids Hunter.

Documentation

• Learning_roadmap.md
• Timing_table.md
• docs/project_taxonomy.md