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AI for IT Engineer

1. Using AI Effectively (Prompting)

Learning AI at this level is like learning how to search effectively on Google—knowing how to ask the right questions. It’s about writing good prompts for GenAI tools and extracting meaningful information from them.
At this stage, we are essentially AI users, much like internet users. Prompt engineering falls into this category, but the real question is: do you actually want to go deep into this, or just practice and improve over time?

2. Understanding How AI Is Built

This level focuses on the fundamentals:

  • How AI and LLMs are architected
  • What models, weights, and training really mean
  • How these systems work under the hood

This path is meant for those who want to become AI researchers or AI developers, or who plan to work directly on building AI/LLM models themselves.

3. Applying AI as an IT Engineer

This is about leveraging AI to build real-world solutions as an IT professional. Examples include using tools and platforms such as:

  • QGenie
  • Copilot
  • Cline
  • n8n
  • A2A, MCP, AI agents, and similar frameworks
  • Claude Code

Here, the focus is not on building models from scratch, but on using AI effectively to solve business and engineering problems.

Some interesting Courses List (highlighted important ones for your convenience)

1. Using AI Effectively (Prompting / AI as a User)

Focus: writing good prompts, interacting with GenAI tools, productivity use cases.

Recommended Courses & Links

(Practical, short, very effective for understanding how to ask better questions)

(Beginner-friendly, business-focused prompting skills)

(Best for Copilot users in enterprise environments)

2. Understanding How AI Is Built (AI Fundamentals & Architecture)

Focus: models, transformers, weights, training, and how LLMs work internally.

Recommended Courses & Links (the videos are one that I would recommend as must watch)

  • What is LLM –
  • What is a neural network – this is where the start understanding of RNN
  • How transformers works – this is what I read to understand full on transformers

https://dev.to/sreeni5018/understanding-transformer-model-types-the-evolution-from-rnn-to-modern-ai-1j4i

https://dev.to/sreeni5018/understanding-the-transformer-architecture-a-studentsjourney-from-classroom-to-exam-hall-11ol

  • How about Image processing –

(Strong foundation, beginner-friendly)

(Covers neural networks, CNNs, RNNs, transformers)

Free lecture videos:

https://www.youtube.com/playlist?list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4

(Deep dive into NLP, transformers, and LLM concepts)

(Hands-on, code-first approach to deep learning)

3. Applying AI as an IT Engineer (AI Engineering / Solution Building)

Focus: building enterprise solutions using AI tools, agents, workflows, and integrations.

(Excellent starting point for LLM-based applications and agents)

(Strong enterprise-grade AI engineering path; note exam retires June 2026)

(Covers RAG, agents, LLM app development)

(Best for AI-driven workflow automation and orchestration)

(Best official starting point; clear explanation of MCP servers, clients, tools, resources)

(Excellent hands-on, open-source MCP course with real integrations)

(Good for enterprise architects and security-aware implementations)

(Very practical, step-by-step MCP server/client implementation)

March 18th, 2026 | Tags: , , | Category: AI

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