Generative AI is transforming industries, revolutionizing how we interact with technology, automate tasks, and building intelligent systems. With large language models (LLMs) at the core of this transformation, there is a growing demand for engineers who can harness their full potential. This Skill Path will equip you with the knowledge and hands-on experience needed to become an LLM engineer. Master core concepts, build confidence for LLM interviews.
Two-Days Large Language Model (LLM) Training Program: Foundations for Developers
This agenda is designed for programmers and application developers, focusing on the conceptual shift from explicit programming to pattern-based machine learning, while introducing the core building blocks of LLMs.
Day 1: The Paradigm Shift and AI Building Blocks (Conceptual
Focus)
Module 1: From Program to Model: 10 AM to 12 PM (120 min)
- Connecting Math to AI:
o Vectors, Matrices, and Tensors: Data structures for LLMs.
o Matrix dot product and Linear Transformation: The simple engine of every neural
layer (how data is transformed).
o Nonlinear Functions: GELU, Tanh, ReLU, Sigmoid
o Probability and statistics: Softmax and decision taking - The Paradigm Shift:
o Traditional Programming: Explicit rules and algorithms.
o Machine Learning: Learning patterns (weights) from seen data and generalization
to unseen data.
o Regression and Classification
Break And Doubt Clarification: 12.00 PM to 12.30 PM (30 Min)
Module 2: The Neural Network Concept: 1PM to 3 PM (120 min)
- The whole thing: Model Weighs and its operators
- The Basic Unit: The Perceptron (the single neuron).
- The Engine: Artificial Neural Networks (ANN) – layers and weights.
- The Learning Process:
o Forward Pass: Making a prediction.
o Loss Function & Backpropagation: How the model adjusts weights based on
errors/loss.
Break And Doubt Clarification: 3.00 PM to 3.30 PM (30 Min)
Module 3: Introduction to Large Language Models (LLMs): 3.30 PM to 5.00 PM (90 min)
- What is an LLM Model? Understanding it as a massive, probabilistic prediction engine
(a function with millions of parameters). - Introduction to NLP (BERT Pipeline, Applications of BERT)
- Introduction to GenAI (ChatGPT Pipeline, Applications of ChatGPT)
Break And Doubt Clarification: 5.00 PM to 5.30 PM (30 Min)
Module 4: The Transformer Architecture – Core Building Blocks: 5.30 PM to 6.30 PM (60
min)
- Embedding and Tokenization:
o Words as Numbers: The role of Word2Vec (conceptually).
o Tokenization (BPE): How the model reads text (sub-word units). - The Importance of Order: Positional Encoding (why it’s needed).
Break And Doubt Clarification: 6.30 PM to 7.30 PM (60 Min)
Day 2: Transformer Models, Customization, Deployment, and
Practical Applications (Theory & Lab) : Build your own ChatGPT
Module 5: The Transformer Architecture continued – Core Building Blocks: 10. AM to 12.00
PM (120 min)
- Attention Mechanism (The Core Concept):
o Self-Attention: Allowing the model to focus on relevant words in the input.
(Focus on what it does: contextual weighting).
o Multi-Head Attention: Looking at context in multiple ways simultaneously. - The Final Layers:
o Feed Forward Networks (FFN): Adding complexity and non-linearity.
o Normalization, Dropout and Residual Connections: Keeping the massive
network stable.
Break And Doubt Clarification: 12.00 PM to 12.30 PM (30 Min)
Module 6: Adapting LLMs for Specific Tasks: 1.00 PM to 3.00 PM (120 min)
(Theory & Lab):
- The LLM Lifecycle:
o Pretraining: The massive initial training phase (unsupervised learning).
o Supervised Fine-tuning (SFT): Adapting the base model to follow instructions
(the “chatbot” stage).
o Knowledge Distillation: The concept of transferring knowledge from a large
model to a smaller, faster one. - Lab Session: Conceptualizing Model Tuning
o Demonstration/Walkthrough: High-level overview of a Pre-training environment
and dataset preparation.
o Demonstration/Walkthrough: High-level overview of a fine-tuning environment
and dataset preparation.
o Demonstration/Walkthrough: High-level overview of a Knowledge Distillation
environment and dataset preparation.
Break And Doubt Clarification: 3.00 PM to 3.30 PM (30 Min)
Module 7: The Deployment Pipeline – RAG and Prompting: 3.30 PM to 5.00 PM (90 min –
Theory & Lab)
- Retrieval-Augmented Generation (RAG): Grounding LLMs in your data.
o The Problem: Hallucination and out-of-date information.
o The Solution: RAG—The Retrieval, Augmentation, and Generation steps.
o The Components: Vector Embeddings and Vector Databases (Explained simply). - Lab Session: Hands-on with RAG
o Demonstration of a basic RAG flow using an existing lightweight framework
(showcasing the data ingestion and retrieval).
Break And Doubt Clarification: 5.00 PM to 5.30 PM (30 Min)
Module 8: Prompt Engineering: 5.30 PM to 6.30 PM (60 min – Theory & Lab)
- Prompt Engineering: The New API:
o Techniques: Zero-shot, Few-shot, and Chain-of-Thought (CoT).
o Strategies for consistent outputs (system instructions, JSON output). - Lab Session: Hands-on with Prompting
o Hands-on exercises with various prompting techniques using a public API
(conceptual setup).
Break And Doubt Clarification: 6.30 PM to 8.00 PM (90 Min)
Lab Session
Two days is about building a strong conceptual foundation, especially for those coming from a
traditional application development background. The focus is on the why and the what, not the
deep mathematical how.
Lab Part 1: Demonstration/Walk-through:
- High-level view of a Hugging Face training script or a Google Colab notebook using a
simplified library like Unsloth or Tensors/PEFT (Python is the common language). - Emphasize the structure of the tuning dataset (input/output pairs) and the concept of
adapter weights.
Lab Part 2: RAG Flow Demonstration:
- Framework: Use LangChain or LlamaIndex (Python frameworks widely used by
developers). - Demonstration: Walk through a 5-step RAG script:
- Load a simple document (text/PDF).
- Chunking the document.
- Generating Embeddings (using a simple model like all-MiniLM-L6-v2).
- Indexing into a lightweight, local Vector Store (like Chroma or FAISS).
- Retrieving context and generating the final response.
Lab Part 3: Hands-on with Prompting
- Hands-on Prompting:
o Tool: Use a public API Playground (e.g., Gemini Playground or OpenAI
Playground).
o Exercise: Implement Zero-shot and CoT prompt by modifying the system
instruction and user input fields.
<section style="font-family: Arial, sans-serif; line-height:1.6; colour:#222; max-width:1100px; margin:auto;">
<!-- HEADER -->
<div style="text-align:center; padding:30px 20px; background:#0f172a; color:#fff; border-radius:10px;">
<h1 style="margin-bottom:10px;">Become an LLM Engineer</h1>
<h3 style="margin:5px 0;">Two-Day Large Language Model (LLM) Training Program</h3>
<p style="margin-top:10px; font-size:16px;">
<strong>📅 Jan 24–25, 2026</strong>
</p>
<div style="margin-top:15px; display:inline-block; padding:12px 20px; background:#22c55e; color:#000; font-weight:bold; border-radius:8px; font-size:18px;">
🚀 BUILD YOUR OWN ChatGPT
</div>
</div>
<!-- INTRO -->
<div style="padding:30px 20px;">
<p>
Generative AI is transforming industries, revolutionizing how we interact with technology, automate tasks, and build intelligent systems.
With Large Language Models (LLMs) at the core of this transformation, there is a growing demand for engineers who can harness their full potential.
</p>
<p>
This skill path equips programmers and application developers with strong conceptual foundations and hands-on exposure to confidently work with LLMs and prepare for interviews.
</p>
</div>
<!-- INTRO SESSION -->
<div style="padding:15px 20px; background:#f1f5f9; border-left:5px solid #2563eb; margin-bottom:30px;">
<strong>Introduction:</strong> 9:45 AM – 10:00 AM (15 mins)
</div>
<!-- DAY 1 -->
<h2 style="padding:10px 20px; background:#e2e8f0; border-radius:6px;">Day 1: Paradigm Shift & AI Building Blocks</h2>
<!-- MODULE 1 -->
<div style="padding:20px;">
<h3>Module 1: From Program to Model</h3>
<p><strong>⏰ 10:00 AM – 12:00 PM (120 mins)</strong></p>
<ul>
<li>Vectors, Matrices & Tensors – data structures for LLMs</li>
<li>Matrix dot product & linear transformation</li>
<li>Non-linear functions: ReLU, GELU, Tanh, Sigmoid</li>
<li>Softmax, probability & decision making</li>
</ul>
<p style="background:#fff7ed; padding:10px; border-left:4px solid #f97316;">
🧠 Break & Doubt Clarification: 12:00 PM – 12:30 PM
</p>
</div>
<!-- MODULE 2 -->
<div style="padding:20px;">
<h3>Module 2: Neural Network Concepts</h3>
<p><strong>⏰ 1:00 PM – 3:00 PM (120 mins)</strong></p>
<ul>
<li>Traditional Programming vs Machine Learning</li>
<li>Regression & Classification</li>
<li>Perceptron & Artificial Neural Networks (ANN)</li>
<li>Forward pass, loss function & backpropagation</li>
</ul>
<p style="background:#fff7ed; padding:10px; border-left:4px solid #f97316;">
🧠 Break & Doubt Clarification: 3:00 PM – 3:30 PM
</p>
</div>
<!-- MODULE 3 -->
<div style="padding:20px;">
<h3>Module 3: Introduction to Large Language Models</h3>
<p><strong>⏰ 3:30 PM – 5:00 PM (90 mins)</strong></p>
<ul>
<li>LLMs as probabilistic prediction engines</li>
<li>NLP foundations – BERT pipeline & use cases</li>
<li>Generative AI – ChatGPT pipeline & applications</li>
</ul>
<p style="background:#fff7ed; padding:10px; border-left:4px solid #f97316;">
🧠 Break & Doubt Clarification: 5:00 PM – 5:30 PM
</p>
</div>
<!-- MODULE 4 -->
<div style="padding:20px;">
<h3>Module 4: Transformer Architecture – Basics</h3>
<p><strong>⏰ 5:30 PM – 6:30 PM (60 mins)</strong></p>
<ul>
<li>Embeddings & Word2Vec (conceptual)</li>
<li>Tokenization (BPE)</li>
<li>Positional Encoding & sequence order</li>
</ul>
<p style="background:#fff7ed; padding:10px; border-left:4px solid #f97316;">
🧠 Break & Doubt Clarification: 6:30 PM – 7:30 PM
</p>
</div>
<!-- DAY 2 -->
<h2 style="padding:10px 20px; background:#e2e8f0; border-radius:6px;">
Day 2: Transformers, Customization, Deployment & Labs
<span style="color:#16a34a;">(Build Your Own ChatGPT)</span>
</h2>
<!-- MODULE 5 -->
<div style="padding:20px;">
<h3>Module 5: Transformer Architecture – Attention</h3>
<p><strong>⏰ 10:00 AM – 12:00 PM</strong></p>
<ul>
<li>Self-attention & contextual weighting</li>
<li>Multi-head attention</li>
<li>FFN, normalization, dropout & residuals</li>
</ul>
</div>
<!-- MODULE 6 -->
<div style="padding:20px;">
<h3>Module 6: Adapting LLMs for Tasks (Theory & Lab)</h3>
<p><strong>⏰ 1:00 PM – 3:00 PM</strong></p>
<ul>
<li>Pretraining, SFT & Knowledge Distillation</li>
<li>Dataset preparation walkthroughs</li>
<li>Model tuning concepts</li>
</ul>
</div>
<!-- MODULE 7 -->
<div style="padding:20px;">
<h3>Module 7: Deployment Pipeline – RAG</h3>
<p><strong>⏰ 3:30 PM – 5:00 PM</strong></p>
<ul>
<li>RAG architecture & hallucination control</li>
<li>Vector embeddings & vector databases</li>
<li>Live RAG flow demonstration</li>
</ul>
</div>
<!-- MODULE 8 -->
<div style="padding:20px;">
<h3>Module 8: Prompt Engineering (Hands-on)</h3>
<p><strong>⏰ 5:30 PM – 6:30 PM</strong></p>
<ul>
<li>Zero-shot, Few-shot & Chain-of-Thought</li>
<li>System instructions & structured outputs</li>
<li>Hands-on using public API playgrounds</li>
</ul>
</div>
<!-- FOOTER HIGHLIGHT -->
<div style="margin:40px 20px; padding:25px; background:#dcfce7; border:2px dashed #22c55e; border-radius:10px; text-align:center;">
<h2 style="margin-bottom:10px;">🎯 Outcome</h2>
<p style="font-size:18px; font-weight:bold;">
Strong conceptual clarity • Real-world LLM workflows •
<span style="color:#15803d;">Build Your Own ChatGPT</span>
</p>
</div>
</section>