Training on Introduction to Generative AI and Large Language Models (LLMs)

Module 1: Introduction to Generative AI

• Understanding generative AI and its evolution
• Generative models: GANs, VAEs, and transformers
• Capabilities and limitations of generative AI
• Generative AI applications across industries
• Ethical considerations in generative AI
• Case Study: Exploring generative AI applications

Module 2: Large Language Models (LLMs) Fundamentals

• Understanding LLMs and transformer architecture
• Key LLMs: GPT, BERT, LLaMA, Claude, and others
• LLM training: pre-training and fine-tuning
• LLM capabilities: text generation, summarization, translation
• Measuring LLM performance and quality
• Case Study: Comparing LLM capabilities

Module 3: Working with LLMs: APIs and Tools

• Accessing LLMs via APIs: OpenAI, Anthropic, Google
• LLM tools and platforms: LangChain, Hugging Face
• Building applications with LLMs
• LLM integration best practices
• Cost and performance considerations
• Case Study: Building a simple LLM-powered application

Module 4: Advanced LLM Techniques

• Retrieval-augmented generation (RAG)
• Fine-tuning LLMs for specific domains
• LLM evaluation and bias mitigation
• LLM security and safety considerations
• LLM scaling and deployment strategies
• Case Study: Implementing RAG for a domain-specific application

Module 5: Generative AI and LLM Applications

• Generative AI for content creation
• LLMs for customer service and chatbots
• LLMs for code generation and software development
• LLMs for research and knowledge management
• Future trends and emerging applications
• Case Study: Designing a generative AI solution for a business need