How does RAG technically work for finding specific information (e.g., current law)?
sobes.tech AI
Answer from AI
Retrieval-Augmented Generation (RAG) is an approach that combines generative models with a mechanism for searching relevant information from external sources, which is especially useful for finding specific and up-to-date information, such as laws.
Technically, RAG works as follows:
-
User query is transformed into a vector representation (embedding) using an encoder model.
-
Searching the document database (e.g., current laws) is performed using nearest neighbor search in the vector space to find the most relevant documents.
-
The retrieved documents are passed along with the original query into a generative model (e.g., transformer), which forms a response based on them.
-
The generative model uses the context from the found documents to create accurate and substantiated answers, rather than relying solely on internal knowledge.
Thus, RAG provides:
- Relevance: searching for fresh information in the database.
- Accuracy: generating answers based on real documents.
- Flexibility: the ability to work with different types of data and queries.
Example: when asked "Which law regulates labor relations in 2024?" the system first finds the latest versions of relevant laws, and then forms a detailed answer based on them.