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LLM Fundamentals Course

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Build strong expertise in Large Language Models (LLMs), transformer architecture, prompt engineering, embeddings, fine-tuning, and AI workflows with hands-on practical sessions. The LLM Fundamentals Course by Multisoft AI helps professionals understand modern AI systems and develop real-world generative AI applications for enterprise use cases.

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The LLM Fundamentals Course by Multisoft AI is designed to help professionals understand the foundations of Large Language Models and modern generative AI technologies. This industry-focused training introduces learners to transformer architecture, tokenization, embeddings, attention mechanisms, prompt engineering, fine-tuning concepts, and inference workflows used in today’s AI-powered applications.

Participants gain practical exposure to popular LLM ecosystems such as OpenAI, Claude, Gemini, and open-source models. The course also covers Retrieval-Augmented Generation (RAG), vector databases, AI APIs, LangChain basics, and ethical AI considerations. Through real-world examples and hands-on exercises, learners explore how LLMs are used for chatbots, content generation, automation, summarization, coding assistance, enterprise search, and intelligent business applications.

This training is suitable for developers, AI engineers, data professionals, automation specialists, and technology enthusiasts looking to build a strong foundation in generative AI. By the end of the program, learners will be able to understand LLM workflows, create effective prompts, integrate AI models into applications, and confidently work on modern AI-driven projects across industries.

  • Evolution of Artificial Intelligence
  • Introduction to Machine Learning and Deep Learning
  • What is Generative AI?
  • Applications of Generative AI
  • AI Trends and Industry Adoption
  • Challenges and Opportunities of AI
  • Future of Generative AI

  • What are Large Language Models?
  • Evolution of Language Models
  • How LLMs Work
  • Capabilities of Modern LLMs
  • LLM Use Cases Across Industries
  • Strengths and Limitations of LLMs
  • Overview of Popular LLMs

  • Introduction to NLP
  • Text Processing Concepts
  • Tokenization Fundamentals
  • Language Understanding Techniques
  • Named Entity Recognition
  • Text Classification
  • Sentiment Analysis Basics

  • Evolution from RNNs to Transformers
  • Attention Mechanism Concepts
  • Self-Attention Architecture
  • Encoder and Decoder Models
  • Positional Encoding
  • Transformer Training Process
  • Benefits of Transformer-Based Models

  • Understanding Tokens
  • Tokenization Techniques
  • Embeddings and Vector Representations
  • Semantic Similarity Concepts
  • Context Windows Explained
  • Prompt Context Management
  • Practical Examples of Embeddings

  • Model Pre-Training Process
  • Self-Supervised Learning
  • Foundation Models
  • Fine-Tuning Fundamentals
  • Instruction Tuning
  • Transfer Learning Concepts
  • Domain Adaptation Strategies

  • Introduction to Prompt Engineering
  • Prompt Structures and Components
  • Zero-Shot Prompting
  • One-Shot Prompting
  • Few-Shot Prompting
  • Contextual Prompting Techniques
  • Prompt Optimization Strategies

  • Understanding Model Inference
  • Text Generation Process
  • Sampling Techniques
  • Temperature and Top-P Parameters
  • Response Control Methods
  • Hallucinations and Their Causes
  • Improving Output Quality

  • Model Evaluation Frameworks
  • Accuracy and Relevance Assessment
  • Benchmarking Concepts
  • Response Quality Evaluation
  • Bias and Fairness Assessment
  • Hallucination Detection
  • Continuous Model Improvement

  • Introduction to RAG
  • Why RAG is Important
  • Knowledge Retrieval Concepts
  • Vector Databases Overview
  • Semantic Search Fundamentals
  • RAG Workflow Architecture
  • RAG vs Fine-Tuning
  • Understand the fundamentals of Large Language Models (LLMs)
  • Learn transformer architecture and attention mechanisms
  • Explore tokenization, embeddings, and vector representations
  • Gain knowledge of prompt engineering techniques
  • Understand how generative AI models process and generate content
  • Learn the basics of Retrieval-Augmented Generation (RAG)
  • Work with AI APIs such as OpenAI, Claude, and Gemini
  • Understand vector databases and semantic search concepts
  • Explore LangChain fundamentals and AI workflows
  • Learn fine-tuning and model customization concepts
  • Build practical AI-powered applications and chatbots
  • Understand ethical AI practices and responsible AI usage
  • Gain hands-on exposure to real-world generative AI use cases
  • Prepare for careers in Generative AI and LLM technologies

LLMs are advanced AI models trained on large datasets to understand and generate human-like text, enabling applications such as chatbots, content generation, summarization, translation, and intelligent assistants.

No. While basic programming knowledge is helpful, the course is designed to accommodate both technical and non-technical professionals interested in understanding LLM technologies.

The course covers the concepts behind leading LLMs such as ChatGPT, Claude, Gemini, Llama, and other transformer-based language models.

Yes. Participants will work with prompts, AI APIs, embeddings, and practical LLM use cases to gain real-world experience.

You will understand how LLMs work, create effective prompts, evaluate AI outputs, integrate AI capabilities into applications, and pursue advanced topics such as RAG, AI agents, and fine-tuning.

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