Large Language Model Development Training Course

Large Language Model Development Training Course

Introduction

Large Language Model (LLM) Development Training Course is a comprehensive, industry-focused program designed to equip professionals with advanced skills in Artificial Intelligence (AI), Natural Language Processing (NLP), Deep Learning, Transformer Architectures, Generative AI, Foundation Models, and Machine Learning Engineering. As organizations increasingly adopt AI-powered automation, intelligent assistants, enterprise copilots, and domain-specific language models, this course provides practical knowledge required to design, develop, fine-tune, deploy, and optimize modern LLM solutions. Participants will explore the complete LLM lifecycle, including data preparation, model architecture design, training pipelines, reinforcement learning, prompt optimization, retrieval-augmented generation (RAG), model evaluation, and responsible AI governance.

This advanced training program combines theoretical foundations with hands-on implementation using modern AI frameworks and cloud platforms. Through real-world projects and case studies, learners will understand how leading organizations build scalable enterprise-grade LLM applications, including conversational AI systems, intelligent search platforms, AI agents, knowledge management solutions, and automated business workflows. The course emphasizes LLM engineering, MLOps, AI safety, model scalability, ethical AI development, and production deployment, enabling participants to create innovative AI solutions aligned with current industry demands.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand the fundamentals of Large Language Models, Generative AI, and Foundation Model architectures. 
2. Design and implement Transformer-based neural network architectures for language intelligence. 
3. Develop advanced Natural Language Processing (NLP) applications using modern AI techniques. 
4. Build and optimize LLM training pipelines using large-scale datasets. 
5. Apply data engineering and data preprocessing strategies for AI model development. 
6. Perform LLM fine-tuning, instruction tuning, and domain adaptation. 
7. Implement Retrieval-Augmented Generation (RAG) systems for enterprise AI applications. 
8. Develop scalable AI agents and autonomous language-based systems. 
9. Apply prompt engineering and advanced prompt optimization techniques. 
10. Evaluate LLM performance using AI benchmarking, validation, and quality metrics. 
11. Deploy production-ready LLM applications using MLOps and cloud AI platforms. 
12. Implement responsible AI, security, privacy, and governance frameworks. 
13. Design innovative business solutions using next-generation AI technologies. 

Target Audience

1. AI engineers and machine learning developers 
2. Data scientists and data analysts 
3. Software engineers building AI applications 
4. Cloud architects and DevOps professionals 
5. Research scientists in AI and NLP 
6. Business technology leaders and innovation managers 
7. Product managers developing AI-powered solutions 
8. Enterprise architects and digital transformation professionals 

Course Modules

Module 1: Foundations of Large Language Models

• Evolution of AI, NLP, and Generative AI technologies 
• Understanding Foundation Models and LLM ecosystems 
• Large-scale language modeling concepts and architectures 
• Overview of GPT, BERT, LLaMA, and other modern LLM families 
• AI infrastructure requirements for LLM development 
• Case Study: OpenAI GPT Model Evolution

Module 2: Transformer Architecture and Deep Learning for LLMs

• Transformer architecture fundamentals 
• Attention mechanisms and self-attention models 
• Encoder-decoder architectures 
• Embedding models and tokenization strategies 
• Neural network optimization techniques 
• Case Study: Google BERT Implementation

Module 3: Data Engineering for LLM Development

• Large-scale dataset collection and preparation 
• Data cleaning, filtering, and augmentation 
• Text preprocessing and token generation 
• Data quality management for AI models 
• Building efficient AI data pipelines 
• Case Study: Enterprise Knowledge Dataset Development

Module 4: Training and Fine-Tuning Large Language Models

• Pre-training methodologies for LLMs 
• Supervised fine-tuning techniques 
• Instruction tuning and alignment strategies 
• Parameter-efficient fine-tuning (PEFT) 
• LoRA and optimization approaches 
• Case Study: Domain-Specific Healthcare LLM

Module 5: Prompt Engineering and LLM Application Development

• Advanced prompt engineering techniques 
• Few-shot and zero-shot learning approaches 
• Chain-of-thought and reasoning strategies 
• Prompt optimization frameworks 
• Building intelligent AI assistants 
• Case Study: Enterprise AI Copilot Development

Module 6: Retrieval-Augmented Generation (RAG) and Knowledge Systems

• Fundamentals of RAG architecture 
• Vector databases and semantic search 
• Embeddings and knowledge retrieval 
• Hybrid search strategies 
• Building enterprise knowledge assistants 
• Case Study: Banking Customer Support AI System

Module 7: LLM Deployment, MLOps, and Scaling

• Production deployment of LLM applications 
• Cloud-based AI infrastructure 
• Model monitoring and performance optimization 
• LLM API integration 
• AI lifecycle management and automation 
• Case Study: Enterprise AI Platform Deployment

Module 8: Responsible AI, Security, and Future LLM Innovations

• AI ethics and responsible model development 
• Bias detection and mitigation strategies 
• LLM security and privacy protection 
• Model evaluation and governance frameworks 
• Future trends in autonomous AI systems 
• Case Study: Responsible AI Governance Framework

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.