AI 용어집
인공지능 완전 사전
Conversational Language Models
Large Language Models (LLMs) specifically optimized for dialogue and conversational interactions.
Context Management Systems
Mechanisms and architectures for maintaining and utilizing conversational history across multiple turns.
Conversational RAG Architecture
Systems combining information retrieval and response generation for knowledge-based dialogues.
Intent Recognition and Entities
Automatic detection of user objectives and extraction of key information in conversations.
Dialogue State Management
Dynamic tracking and updating of conversation state to maintain consistency.
Contextual Response Generation
Techniques for automatically producing relevant responses adapted to the conversational context.
Multimodal Dialogue
Integration of text, voice, images and other modalities in conversational systems.
Conversational Personalization
Adaptation of responses and conversational style according to user profile and preferences.
Emotional Dialogue Systems
Recognition and generation of emotions to create more natural and empathetic conversations.
Dialogue Evaluation and Metrics
Methods and indicators for measuring the quality, relevance, and consistency of conversations.
Multi-turn Conversational Agents
Systems capable of maintaining complex and extended conversations across multiple exchanges.
Optimization of Conversational Prompts
Specific prompt engineering techniques to improve model performance in dialogue.
Multilingual Dialogue Systems
Architectures enabling fluid conversations across multiple languages and cultures.
Hybrid Dialogue Architectures
Combination of rule-based systems and AI for robust and controlled conversations.
Conversation Coherence Management
Mechanisms ensuring logical and thematic consistency throughout the conversation.