Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG)
RAG (Retrieval-Augmented Generation) is an AI approach that merges the power of traditional information retrieval systems, like search engines and databases, with the abilities of generative large language models (LLMs). This integration allows for more precise, current, and contextually relevant generation by combining both your data and general world knowledge with the language proficiency of LLMs.
A Retrieval-Augmented Generation (RAG) system is a powerful architecture for enhancing the performance of language models by integrating external information through retrieval. It allows you to generate more accurate, informative, and contextually relevant answers.
What is RAG?
Retrieval-augmented generation (RAG) is an AI framework that combines two major components:
- Retrieval: Searching for and fetching relevant information from a database or knowledge source.
- Generation: Using a language model (like GPT or BERT) to generate human-like text based on the retrieved information.
Instead of relying solely on a pre-trained language model to generate answers in an RAG system, the system first retrieves relevant documents or information from an external source (like a database, document collection, or search engine). It then uses a generation model to craft an answer using both the query and the retrieved data.
Components of a RAG System
The main components of an RAG system are as follows:
Retriever: This component searches through a large collection of documents or knowledge bases (e.g., databases, external files, or APIs) to find the most relevant pieces of information that can answer the query.
Generator: After retrieving the information, a language model or text generator processes the input (query + retrieved documents) and generates a well-structured and natural language response.
How Does a RAG System Work?
The simplified breakdown of the RAG system is as follows:
Input Query: The user asks a question (e.g., “What are the latest advancements in AI?”).
Retrieval Step:
The retriever searches a knowledge source (e.g., Wikipedia, a document store, or a web search) for the most relevant documents or information related to the question.
Generation Step:
The generator (a large language model) takes the retrieved information and the original query and generates a coherent and contextually accurate answer.
Output: The system provides the generated response to the user.
- 1 -> Prompt
- 2 -> Retrieval query
- 3 – > Datasets
- 4 -> Prompt _ datasets
- 5 -> Response
- 6 -> Output
Why Use RAG?
The main advantage of RAG is that it allows the system to generate more accurate and contextually relevant responses by augmenting the model’s knowledge with external information. This is particularly helpful in domains where the model might not have all the knowledge (e.g., up-to-date facts, domain-specific knowledge).
Search engines: A query could retrieve relevant documents, and then a language model could summarize those documents or answer questions based on the content.
Customer support: A system could fetch relevant product information or FAQ answers and then generate a helpful response to the user.