# Navigating the LLM Landscape: A Practical Implementation Guide for Engineers

Join us for a focused, engineer-friendly deep-dive into modern Large Language Models (LLMs) and their practical applications in network engineering, operations, and automation.  
This weekly workshop series is designed to give participants hands-on knowledge, real deployment insights, and actionable skills.

##### Instructor 

Mohammed Bakheet  
[https://www.linkedin.com/in/mrabkoo/](https://www.linkedin.com/in/mrabkoo/)

##### Who Should Join?  


Network engineers, cloud engineers, systems operators, students in ICT, and anyone interested in practical AI applications in technical operations.

##### Workshop Topics

1\. Prompt Engineering  
Learn how to craft effective prompts to get accurate, reliable, and context-aware outputs from LLMs.  
We cover:

- Prompt structure and templates
- System vs. user instructions
- Prompt optimization techniques
- Avoiding hallucinations

2\. RAG (Retrieval-Augmented Generation) Pipelines  
Understand how to integrate your own data sources with LLMs to deliver accurate and domain-specific responses.  
We cover:

- Vector embeddings
- Knowledge bases
- Architecture patterns for production RAG

3\. Fine-tuning LLMs  
Dive into customizing LLMs for your environment, dataset, or operational workflows.  
We cover:

- Types of fine-tuning (SFT, LoRA, QLoRA)
- Data preparation
- Training workflows
- Evaluating and validating tuned models

4\. AI Agents  
Explore how AI agents can automate tasks, perform reasoning, and integrate with systems and APIs.  
We cover:

- Multi-step task execution
- Tool calling
- Agent architectures
- Practical use cases for NOGs and ISPs

##### Why Attend?

- Hands-on, practical focus
- Real examples from engineering and operations
- Learn how to deploy and use LLMs effectively in your environment
- Open discussion, demos, and shared learning with the sdnog community

##### Session's record

[https://drive.google.com/file/d/1\_FUkQgAddEermA45m-1FYyx8tNQi6Itx/view?usp=share\_link](https://drive.google.com/file/d/1_FUkQgAddEermA45m-1FYyx8tNQi6Itx/view?usp=share_link)