Taxonomy of Large Language Model Applications:
A comprehensive classification system that organizes applications powered by Large Language Models (LLMs) based on their primary purpose, functionality, interaction style, and unique characteristics. This taxonomy serves as a valuable resource for developers and startups, facilitating a deeper understanding of the various types of LLM applications and the key considerations associated with their development. The system comprises four main categories: Conversational Agents, Copilots and Duets, Chat with Data or Retrieval Augmented Generation (RAG), and NLP Tasks and Autonomous Agents. By providing a structured framework for exploring and building LLM applications, this taxonomy aims to support innovation and growth within the domain of AI-driven solutions.
- Introduction to LLM App Categories: A classification system proposed by the speaker to help understand and categorize Large Language Model (LLM) applications.
- Conversational Agents: Chatbots and conversational agents that engage in wide-open domain conversations, requiring considerations such as personality definition, conversation engagement, and outcomes.
- Copilots and Duets: Apps that use customer data to assist in achieving goals, either embedded in software or guiding educational journeys, with opportunities for startups in the educational app space.
- Chat with Data or RAG: Apps involving retrieval augmented generation (RAG) that allow natural language interaction with large data, commonly used for question-answering tasks, with challenges related to competition and high-quality retriever augmentation.
- NLP Tasks and Autonomous Agents: Categories that highlight traditional NLP tasks where LLMs excel, such as sentiment analysis and data extraction, and autonomous agents that automate tasks through reasoning and decision-making, with a need for state-of-the-art models.