Large Language Models

What are Large Language Models?
Large Language Models (LLMs) are a type of artificial intelligence (AI) model that is trained on vast amounts of textual data to understand and generate human-like language. These models are typically built using deep learning architectures, with transformer architectures being a common choice.

What is Retrieval Augmented Generation?
Retrieval Augmented Generation (RAG) is a natural language processing (NLP) paradigm that combines both generative and retrieval-based approaches. In traditional generative models, such as Large Language Models (LLMs) like GPT-3, the system generates text based on learned patterns from a vast amount of data. In contrast, retrieval-based models retrieve and use existing pieces of text, often from a predefined knowledge base, to respond to queries. RAG integrates these two approaches by combining the generative power of LLMs with the retrieval capability of information retrieval systems. The goal is to enhance the relevance, accuracy, and contextuality of generated responses.

Who should attend?

• Business leaders with an interest in applying LLM.
• Entrepreneurs keen on sharing and gaining insights on LLM.
• Technology professionals engaged or exploring LLM.
• Students and researchers who are passionate about LLM.

Event Format

• Presentations, Panel discussions, Workshops, Tutorials, Networking sessions
• Invite Product companies, Open Source communities, and Startups to present
• Interactive and collaborative

Topics Covered

1. What are Large Language Models? Overview, Strengths, Limitations
2. What is Retrieval Augmented Generation (RAG)?
3. Vector Databases - Deep Dive (Pinecone)
4. NLP Use Cases and Large Language Models
5. Business Applications of LLM
6. Technical deep dives into LLM
7. Hands-On Workshops: Implementing LLM Applications
8. Software Development: What can Generative AI automate?

Benefits of Participation

• Networking opportunities with professionals from business and technology.
• Access to the latest trends and developments in Generative AI.
• Opportunities for collaborative projects and partnerships.