GenAI Deep Dive - From Ideas to Impact
GenAI Deep Dive - From Ideas to Impact (1 day)
The use of Generative AI models—particularly ChatGPT—is generating widespread interest. Yet, uncertainty and skepticism around this technology are common. This seminar aims to clear the fog and resolve any questions. In an era shaped by digital transformation and advancing AI, understanding and effectively using Large Language Models can be a key factor in driving innovation and ensuring business success
In this hands-on seminar, participants dive into the world of Generative AI models and learn how to use ChatGPT and similar language models without needing technical expertise, empowering them to actively shape the future. The diverse applications—along with complementary apps—are explained and supported by practical examples. Topics include automating tasks and processes with LLMs, developing or optimizing products, and creating new business models based on this technology. Additionally, the seminar covers future prospects and limitations of AI models. A special segment explores using Large Language Models with internal company data (RAG - Retrieval Augmented Generation).
Course Outline
Basics and Functionality of LLMs
- How ChatGPT and similar language models work
- Potential and limitations of language models
- Overview of LLM use cases
- Questions, dialogues, and commands
- Automating tasks and processes
- Complementary apps
- Product Innovation: Utilize LLMs to generate creative ideas and enhance existing products based on user feedback and market trends.
- Business Model Creation: Explore new revenue streams and value propositions by leveraging LLM capabilities to meet evolving customer needs.
- Iterative Optimization: Implement data-driven insights from LLMs to continuously refine product features and business strategies for sustained growth.
- Comparative Analysis: Key differences between ChatGPT and other leading Large Language Models.
- Use Cases and Applications: Practical examples of how other LLMs are applied across industries.
- Future Trends in AI: Insights into the evolving landscape of AI technologies.
- LLMs in Knowledge Management: Role of Large Language Models in modernizing internal data analysis with contextual insights.
- Embedding Techniques for Contextual Understanding: How embeddings transform text into vectors, enabling semantic search and retrieval.
- RAG (Retrieval-Augmented Generation) Workflows: Integrating RAG for precise information retrieval and enhanced response generation.
- Vector Databases for Efficient Data Storage: Storing and querying embeddings with vector databases for scalable, real-time insights.
- Graph Databases for Relationship Mapping: Leveraging graph databases to visualize connections and enhance relational insights across data.
- Introduction to AI Agents: Overview of AI agents and their autonomous, goal-driven functionality within generative AI applications.
- Components and Architecture of AI Agents: Introduction to core components like perception, reasoning, and action that enable agents’ autonomous behavior.
- Agent Objectives and Task-Oriented Design: Designing agents with clear goals and flexible learning for task-specific, adaptable performance.
- Ethics, Safety, and Alignment in Autonomous AI Agents: Ensuring agent behaviors align with ethical guidelines and human intentions for safe, responsible use.
- Practical Applications and Case Studies: Real-world examples of AI agents across industries, demonstrating their capabilities, challenges, and impacts.
Benefits
- Understanding the technological foundations of LLMs
- Gaining knowledge of its applications to optimize processes
- Develop innovative business ideas, and drive digital transformation forward
Who should attend
Professionals and executives who want to explore the possibilities of Generative Artificial Intelligence and apply it in their professional environment. No technical expertise required.
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