A Comprehensive Understanding of Context: Four Major Modes of Interaction in LLM-based GenAI Product Interactions and Their Applications in Technology Practice
In the realm of artificial intelligence, particularly with the proliferation of Large Language Models (LLMs), the diversity and complexity of generative AI product interactions continue to expand. With technological advancements, four primary modes of human-machine interaction have emerged: the RAG model, ChatBOT mode, AI-driven menus/function buttons, and generative AI-driven process and dataflow integration into IT systems. This article will delve into these four interaction modes, outlining their characteristics, technological implementations, and their application prospects in both business and technological development.
1. RAG Model (Referential-Aware, Gap-filled)
The RAG model stands as a pivotal mode of interaction in LLM-based GenAI product interactions, capable of integrating multidimensional information while incorporating external knowledge in collaboration with foundational LLM knowledge repositories. In this mode, the system not only comprehends user inquiries or commands but also engages in recombination and content generation. The P-version module within HaxiTAG Studio operates on the principles of RAG. This mode underscores the synergy between external knowledge and internal foundational knowledge repositories, enhancing interaction experiences with richness and precision.
2. ChatBOT Mode
Similar to ChatGPT or POE, the ChatBOT mode emphasizes the omniscient nature of AI agents in information acquisition and processing. Under this mode, all interactions are facilitated by the agent, which must exude confidence and possess an extensive breadth of knowledge to obviate the need for explanations from the user, implicitly fostering a logic of entrusting information trust. Nonetheless, this also contributes to users' relatively low tolerance for its imperfections.
3. Copilot plug-in, an Independent AI-Driven Function application
Outside the existing software systems, Copilot serves as an autonomous auxiliary software tool.Copilot provides intelligent assistance, emphasizing the availability of support for users of software systems. Its core advantage lies in providing necessary aid without compromising the autonomous judgment and decision-making of the application operator. The design philosophy of Copilot is to make software system operators feel as though they have a knowledgeable colleague nearby, ready to assist in problem-solving or offer suggestions. Additionally, through integration with the Copilot plugin provided by the cursor, it introduces RAG technology, an intelligent knowledge retrieval system. RAG can offer real-time code explanations, knowledge inquiries, and display various coding styles, enabling developers to write code more efficiently during the learning and adaptation process.
This experience with Copilot not only simplifies complex software system operations such as business processing, data management, and operational tasks but also provides developers with a powerful tool outside the software system environment, assisting them in guiding and resolving issues more effectively.
4. Classical software menu and function by Generative AI-Driven Process and Dataflow
Integrating generative AI-driven processes and data flows into traditional IT systems not only enables more flexible and adaptive interaction experiences but also addresses forward compatibility concerns in software applications. However, this approach introduces challenges related to the uncertain feedback of Generative AI, necessitating the design of new interface containers for presentation. By embedding AI-driven logic within existing IT systems, traditional software engineering and system interaction interfaces retain their familiar UI/UX while integrating AI functionality as a core element, thereby enhancing interaction intelligence through AI-driven augmentation.
As LLM-based generative AI product interaction technology continues to advance, we witness an increasingly expansive landscape of application prospects in both business and technological realms. The RAG model, ChatBOT mode, AI-driven menus/function buttons, and generative AI-driven process and dataflow interactions each possess unique advantages and application scenarios, further propelling the development boundaries of human-AI interaction.
Related Topic
Artificial Intelligence, Large Language Models, GenAI Product Interaction, RAG Model, ChatBOT, AI-Driven Menus/Function Buttons, IT System Integration, Knowledge Repository Collaboration, Information Trust Entrustment, Interaction Experience Design, Technological Language RAG, HaxiTAG Studio, Software Forward Compatibility Issues.