Retrieval Augment Generation
Grounding an LLM and get better outputs
Retrieval-Augmented Generation (RAG) represents an important step forward in the development of AI systems capable of generating human-like text that is both relevant and informed by a vast array of external data, making them more useful for a wide range of applications.
RAG is a method that combines the power of neural language models with information retrieval to enhance the generation of text. RAG makes the language models more informative, accurate, and relevant by allowing them to access and incorporate external knowledge or data during the text generation process.
Retrieval-Augmented Generation (RAG) works by integrating a retrieval component into a generative model, allowing the system to pull in external information during the generation process. This approach enhances the model’s ability to produce accurate, relevant, and informed output. The technical workings of RAG involve several key steps and components:
Retrieval Component
When given a prompt or question, the RAG system first performs a search across a large corpus of documents or a database to find relevant information. This step is crucial because it determines the quality and relevance of the information that will be used for generating the response.
Document Store: RAG uses a large corpus of documents (e.g., Wikipedia, books, or specialized databases) as its external knowledge source. This document store is indexed in advance to facilitate efficient search and retrieval.
Query Formation: For each input prompt or question, the model formulates a query. This step often involves processing the input to extract key terms or concepts that will guide the search.
Search and Retrieval: The retrieval component searches the document store using the query and returns a set of relevant documents or text snippets. The efficiency of this step is crucial, as it affects the overall speed and responsiveness of the model.
Augmentation and Integration
The retrieved documents or pieces of information are then provided to the language model as additional context. This step essentially “augments” the model’s existing knowledge, giving it access to a wider range of information than was available in its initial training data.
Contextualization: The retrieved documents are processed and combined with the original input to form an augmented input. This step may involve encoding the documents and the input into a format that the generative model can understand.
Attention Mechanism: The generative model, often based on the Transformer architecture, uses self-attention mechanisms to integrate the information from the augmented input. It can weigh the importance of information from the input and the retrieved documents, allowing it to focus on the most relevant details.
Generation
Armed with this additional context, the language model generates a response or completes the task, incorporating insights or details from the retrieved information. The final output is thus a blend of the model’s pre-trained knowledge and the specific, relevant information fetched during the retrieval step.
Decoding: The generative model produces output based on the augmented input. This involves predicting the next word or token in the sequence, taking into account both the original input and the information from the retrieved documents.
Iterative Refinement: In some implementations, the generation process can be iterative, with the model refining its output based on additional feedback or further retrieval steps.
Technical Foundations
Neural Networks: Both the retrieval and generative components of RAG are powered by neural networks. The retrieval part often uses a dense vector search, where documents and queries are represented as vectors in a high-dimensional space, and similarity measures are used to find relevant documents.
Transformer Architecture: The generative model typically relies on the Transformer architecture, which excels at handling sequential data and can capture complex relationships within the text.
This technical workflow allows RAG to dynamically incorporate external information, making it highly effective for tasks that require detailed, accurate, and up-to-date knowledge.
Example
1. Prompt: “What are the health benefits of green tea?”
2. Query Formation: The model processes the question to form a query focused on “health benefits” and “green tea.”
3. Retrieval: The system searches the document store and retrieves relevant documents or sections that discuss green tea’s health benefits.
4. Augmentation: The retrieved information is combined with the original question to form an augmented input context.
5. Generation: The model generates an answer, integrating information from the original question and the retrieved documents to provide a comprehensive response.
Advantages of RAG Architecture
Enhanced Knowledge: By retrieving information from external sources, RAG models can provide responses that are more accurate, detailed, and up-to-date than those generated by standalone language models.
Flexibility: The retrieval component can be updated or changed without retraining the entire model, allowing for flexibility and adaptability to new information or data sources.
Efficiency: RAG efficiently uses computational resources by focusing the model’s generative power on integrating and synthesizing information rather than storing vast amounts of data.
RAG Applications:
Question Answering: RAG is particularly effective for question-answering systems, where the accuracy and relevance of the information provided are paramount. It allows the model to fetch and use the most up-to-date information from external sources. Bing Copilot uses RAG architecture.
Content Creation: In tasks requiring detailed and informative content, RAG can help ensure that the generated text is relevant and factually accurate by pulling information from a large corpus.
Enhanced Conversations: Chatbots and conversational agents can use RAG to provide more informative, specific, and contextually appropriate responses.
