Building Your First Large Language Model (LLM) – A Beginner’s Guide (No Coding Required)

By the end of the course, participants will have hands-on experience creating, fine-tuning, and deploying their own LLM using accessible, no-code platforms.
Duration: 1 Day
Hours: 3 Hours
Training Level: All Level
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Batch Two
Friday, October 24, 2025
12:00 PM - 03:00 PM (Eastern Time)
Batch Three
Monday, November 24, 2025
12:00 PM - 03:00 PM (Eastern Time)
Batch Four
Friday, December 19, 2025
12:00 PM - 03:00 PM (Eastern Time)
Live Session
Single Attendee
$149.00 $249.00
Live Session
Recorded
Single Attendee
$199.00 $332.00
6 month Access for Recorded
Live+Recorded
Single Attendee
$249.00 $416.00
6 month Access for Recorded

About the Course:

AI and Large Language Models (LLMs) are revolutionizing industries, automating workflows, and enhancing productivity across multiple domains. However, the complexity of AI development often discourages non-technical individuals from exploring this field.

This course is designed for beginners with no coding experience and will provide a step-by-step guide on how LLMs work, how they can be built using no-code tools, and how to fine-tune them for specific tasks. By the end of the course, participants will have hands-on experience creating, fine-tuning, and deploying their own LLM using accessible, no-code platforms

Course Objective:

  • Understand what LLMs are and how they work (in simple, non-technical terms)
  • Learn the difference between open-source and proprietary LLMs
  • Explore the essential steps involved in building an LLM without coding
  • Learn how to collect and prepare training data for LLMs
  • Gain hands-on experience using no-code AI platforms to build an LLM
  • Fine-tune an LLM for specific tasks such as chatbots and text generation
  • Deploy their custom-trained LLM for real-world use

Who is the Target Audience?

  • Non-technical learners – Anyone with no prior coding or AI experience who wants to understand LLMs
  • Business professionals – Decision-makers looking to integrate AI into their businesses
  • Content creators & marketers – Individuals exploring AI-driven automation for content generation
  • Educators & researchers – Those interested in using AI for knowledge sharing and education
  • AI enthusiasts – Anyone curious about AI and looking for an easy-to-follow introduction

Basic Knowledge:

This course is designed to be completely beginner-friendly, and no prior AI or programming knowledge is required. However, having a basic understanding of the following will be beneficial:

  • Basic Computer Skills – Ability to navigate web-based tools and platforms
  • General Awareness of AI Concepts (Optional) – Helps in understanding real-world applications
  • No Coding or Machine Learning Experience Needed – The course uses no-code platforms to simplify LLM creation

Curriculum
Total Duration: 3 Hours
Understanding the Basics of LLMs

  • What is an LLM?   
    • Introduction to AI and Large Language Models
    • How LLMs are different from traditional AI systems
    • Real-world applications of LLMs
  • How LLMs Learn and Work (Non-Technical Explanation)   
    • Explanation of training data, tokens, and embeddings
    • Pre-training vs fine-tuning in simple terms
    • What makes a model "large"? (Scale, parameters, etc.)
  • Types of LLMs   
    • Open-source LLMs (e.g., GPT-based, BERT, DeepSeek)
    • Proprietary LLMs (e.g., ChatGPT, Bard)
    • Pros and cons of open-source vs proprietary models

The Step-by-Step Process to Create an LLM (Non-Coding Approach)

  • Choosing the Right Approach to Build an LLM   
    • From scratch vs leveraging existing LLMs (distilled models, pre-trained LLMs)Identifying the purpose of your LLM (e.g., customer support chatbot, text summarizer)
  • Gathering and Preparing Data for an LLM (Simplified)   
    • What kind of data is used to train LLMs?
    • Sources of training data (text corpora, documents, etc.)
    • Importance of clean, diverse, and unbiased data
  • Understanding the Training Process (No Code)   
    • How training happens conceptually (explained visually)
    • Key components: epochs, loss function, optimization
    • Cloud-based and automated LLM training solutions (Hugging Face, Google Colab)

Building Your First Custom LLM (Hands-On Walkthrough)

  • Using a No-Code LLM Tool   
    • Introduction to no-code platforms for AI (e.g., Hugging Face AutoTrain, Forefront AI, Runway ML)
    • Setting up a simple LLM project (guided tutorial)
  • Fine-Tuning an LLM for a Specific Task (Simplified)   
    • What is fine-tuning?
    • Uploading your dataset to the platform
    • Training the LLM and monitoring its performance
  • Testing and Deploying Your LLM   
    • Evaluating your model’s performance (accuracy, coherence)
    • Deploying your LLM as a chatbot or API with no-code tools
    • Use case demo: Creating a simple FAQ bot using your trained LLM