Smart Answers, Quick Solutions: Tap the Information with Query Insight!
Agentic Approach with Query Insight
- Self-Management: AI agents can make decisions and take actions on their own.
- Reaction: AI agents can react to external stimuli and changes.
- Planning: AI agents break complex tasks into manageable steps. Determines the sequence of actions necessary to achieve a goal and adapts plans when faced with challenges.
- Proactivity: AI agents can foresee future events and make plans to achieve their goals.
- Control and Learning: AI agents evaluate their own performance, learn from mistakes, and improve their approaches over time.
- Socialization: AI agents can interact with other agents or systems.
- Chain of Thinking: This technique encourages creative problem-solving ability by allowing AI agents to explore different ways of reasoning.
Query Insight can perform complex tasks by working within the framework of the Large Language Model (LLM). It works by combining the abilities of multiple AI agents. Each AI agent is specialized in specific task areas, can communicate with each other and share information, improving overall operating efficiency.
Productive Question Answering
Generative Question Answering is a technology used in the fields of natural language processing (NLP) and artificial intelligence (AI), specifically generating natural language answers to questions asked by users. This technology is accomplished using generative models, often powered by methods such as deep learning and neural networks.
This method is based on the idea that a machine can understand natural language and learn to generate natural language text in a way that provides a correct answer in terms of grammar and meaning. Generative Question Answering, unlike traditional fixed-answer systems, can generate new and original answers depending on the question asked. This means that the model can understand the context of the question and generate an appropriate response, rather than choosing from predefined answers in the database.
Generative QA systems often use deep learning techniques that learn from large amounts of text data and can use that information to answer new questions. These systems are supported by techniques used in a variety of natural language processing (NLP) tasks, such as text summarization, text generation, and natural language understanding.
The advantages of Productive Question Answering include the ability for users to obtain information in a more natural and interactive manner, the ability to respond to a broader and more diverse range of question types, and the capacity to produce detailed and descriptive answers about a particular topic.
Generative Model
Retrieval-Based Model
Discriminative Model
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