技术方法

Retrieval-Augmented Generation (RAG)

定义

A technique that enhances LLM responses by retrieving relevant information from external knowledge sources before generating a response. RAG improves accuracy, reduces hallucinations, and enables LLMs to access up-to-date information without retraining.

示例

Enterprise knowledge base chatbots
Customer support automation

相关术语

Large Language Model (LLM)

AI models trained on vast amounts of text data that can understand and generate human-like text. Examples include GPT-4, Claude, and Llama. LLMs power modern chatbots, content generation, and code assistance tools.

Vector Database

A specialised database designed to store and efficiently search high-dimensional embedding vectors using approximate nearest-neighbour algorithms. Vector databases are the infrastructure backbone of RAG systems and semantic search applications.

Embedding

A numerical representation of data (text, images, etc.) in a high-dimensional vector space where semantically similar items are mathematically close together. Embeddings are the foundational building block for semantic search, RAG pipelines, and recommendation systems.

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Retrieval-Augmented Generation (RAG)

定义

示例

相关术语

Large Language Model (LLM)

Vector Database

Embedding

相关服务

了解术语只是第一步，将其落地应用才是第二步。

Retrieval-Augmented Generation (RAG)

定义

示例

相关术语

Large Language Model (LLM)

Vector Database

Embedding

相关服务

了解术语只是第一步，将其落地应用才是第二步。