2. What skills will I develop from this course?

You can develop skills in LLM fundamentals, prompt engineering, model evaluation, API integration, RAG, embeddings, fine-tuning concepts, and AI agent applications. You will also learn about common LLM limitations and methods for creating safer AI-powered applications.

3. What will I learn about generative AI and LLMs?

The course explains how large language models process and generate language, including transformers, attention, tokens, context windows, pretraining, fine-tuning, and inference. It also explores how LLMs are applied to content generation, data analysis, assistants, question answering, and other AI applications.

4. Does the course cover LLM APIs and application development?

Yes. Learners explore LLM APIs, API requests, model parameters, response processing, application integration, and LLM-powered workflows. The curriculum also introduces RAG, vector databases, embeddings, fine-tuning, and AI agents.

5. How does the course address LLM safety and reliability?

The course covers hallucinations, performance evaluation, bias, toxicity, sensitive information, prompt injection, and responsible deployment. These topics help learners recognize common risks and incorporate appropriate safeguards when developing or using LLM applications.

Large Language Models (LLMs) Course: Learn LLMs and Generative AI

Category Data Analytics & AI

Level Intermediate

Students 17 Already Enrolled

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(4.6)

A large language models course introduces the technologies behind modern generative AI and explains how LLMs process language, generate content, respond to prompts, and support intelligent applications. Learners explore core concepts alongside practical applications across business, technology, research, and digital workflows.

The course covers transformers, attention mechanisms, tokens, context windows, pretraining, fine-tuning, inference, prompting, APIs, retrieval-augmented generation, embeddings, vector databases, and AI agents. Learners also examine how LLMs can support content creation, summarization, question answering, data analysis, and AI-powered assistants.

Safety and evaluation are integrated throughout the course, including hallucinations, bias, toxicity, sensitive information, prompt injection, and responsible deployment. By completing the course, learners develop a strong foundation for evaluating LLM technologies and designing applications that use language models effectively.

Large language model fundamentals, architecture, tokens, and context
Generative AI, transformers, attention, training, and inference
Prompt engineering and optimization techniques
LLM applications, APIs, assistants, and AI-powered workflows
RAG, embeddings, vector databases, fine-tuning, and AI agents
LLM evaluation, security, safety, and responsible deployment

No formal AI or programming qualification is required to enrol. Basic familiarity with computers, digital tools, or artificial intelligence can be helpful, while learners interested in technical development may benefit from some programming experience.

This course is designed for AI enthusiasts, developers, software professionals, data professionals, business users, researchers, and technology teams exploring generative AI. It is also suitable for learners who want to understand LLM technology and its applications without requiring advanced prior knowledge.

1.1 Understanding Large Language Models
1.2 Evolution of Language Models
1.3 How LLMs Process Language
1.4 Tokens, Context, and Parameters
1.5 LLM Capabilities and Limitations

2.1 Understanding Generative AI
2.2 Transformer Architecture Fundamentals
2.3 Attention Mechanisms Explained
2.4 Pretraining and Fine-Tuning
2.5 Model Inference and Generation
2.6 Understanding Model Context Windows

3.1 Exploring Popular LLM Applications
3.2 Using LLM Interfaces
3.3 Writing Effective LLM Prompts
3.4 Managing Conversation Context
3.5 Controlling AI Outputs
3.6 Evaluating LLM Responses

4.1 Zero-Shot Prompting
4.2 Few-Shot Prompting
4.3 Role-Based Prompting
4.4 Structured Output Prompting
4.5 Chain-of-Thought Concepts
4.6 Iterative Prompt Optimization

5.1 LLM-Powered Content Generation
5.2 Natural Language Processing Applications
5.3 Text Summarization and Extraction
5.4 Question Answering Systems
5.5 LLM-Assisted Data Analysis
5.6 Building AI-Powered Assistants

6.1 Understanding LLM APIs
6.2 Sending API Requests
6.3 Managing Model Parameters
6.4 Processing API Responses
6.5 Integrating LLMs into Applications
6.6 Building LLM-Powered Workflows

7.1 Understanding Retrieval-Augmented Generation
7.2 Building Knowledge-Based LLM Applications
7.3 Vector Databases and Embeddings
7.4 Fine-Tuning LLMs
7.5 LLM-Powered AI Agents
7.6 Choosing RAG or Fine-Tuning

8.1 Understanding LLM Hallucinations
8.2 Evaluating LLM Performance
8.3 Managing Bias and Toxicity
8.4 Protecting Sensitive Information
8.5 Preventing Prompt Injection
8.6 Deploying Responsible LLM Applications

A large language models course is suitable for beginners, AI enthusiasts, developers, technology professionals, researchers, data professionals, and business users. Learners can start without advanced technical knowledge, although programming experience can be useful for API and application development topics.

Large Language Models (LLMs) Course: Learn LLMs and Generative AI

About Large Language Models (LLMs) Course: Learn LLMs and Generative AI

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