I have learned about Retrieval-Augmented Generation (RAG), a powerful approach that combines retrieval mechanisms with generative models to enhance response quality.

Knowledge Base
Before retrieval, documents must be ingested and preprocessed. This typically involves breaking down large documents into smaller chunks, converting them into text embeddings, and storing them in a database. This process ensures that relevant information can be easily accessed when needed.

User Query
When a user asks a question, the system initiates the retrieval process. The user query serves as the foundation for retrieving pertinent information from the prepared knowledge base.

Retrieval Process
Upon receiving a query, an embedding model searches the knowledge base for relevant chunks of information. This context is crucial as it enhances the subsequent generation by providing specific, related data that the generative model can utilize in its response.

Augmented Generation
The generative model (法學碩士) uses the retrieved information to enhance its responses. This capability allows the model to not only rely on its pre-trained knowledge but also incorporate real-time, contextually relevant data. Consequently, the model returns more accurate and contextually appropriate answers to user queries.

Why Use RAG?
Information Richness: RAG ensures that text responses are current and relevant, improving performance in specific domain tasks by accessing an internal knowledge base.
Reduction of Hallucinations: By leveraging verified data from the knowledge base, RAG minimizes the risk of generating false or misleading information.
Cost-Effectiveness: Compared to fine-tuning a generative model, RAG can be more economical, making it an attractive option for enhancing AI capabilities.
In summary, RAG enhances the relevancy and accuracy of AI-generated responses while being efficient and effective.