RAG (Retrieval-Augmented Generation) is an artificial intelligence framework that integrates the capability of search engines and databases with the function of LLMs. It also incorporates your input data, in addition to the common general knowledge of the world, to generate better, more suitable, and fresher answers.
RAG gives the model the ability to deploy specific information such as the values of an organization without training the model again. This blog aims to explain what actually RAG is and how it works.
How Does RAG Work?
RAG follows a few simple steps to improve AI-generated responses:
1. Collecting Data
Initially, one has to know the information required for the particular application. For instance, if you’re integrating virtual support for customer support chat in an electronics firm then this means the user guides, the product listing, and a set of FAQs.
2. Chunking Data
The first type of operationalization includes data chunking – subdividing the data into more workable segments. For example, if you have a 100 pages long user manual you could divide the document into segments, each of them separately addressing a different topic or question from a customer.
This made each data chunk contain information about only one subject so when information was searched out it was very likely to be answered to the user.
3. Embedding Documents
After that division has been done on the data, it gets converted into vectors Its simply stated as converting data into vectors. This means extracting the text meanings and turning the words into numbers to give an understanding of what they represent.
In other words, when the system creates document embeddings, it allows the system to link the user’s search queries to the relevant information with regards to the meaning of the document.
4. Processing User Queries
RAG takes two steps in the retrieval process when a user enters a query into the system. First, it narrows the search and then searches other data like web data for the current and accurate data.
Secondly, it’s system evaluates both sets of the results selecting the most relevant, up-to-date, and accurate information. Moreover, RAG updates its vector database with new information and refined user queries.
5. Generating Responses with LLM
The retrieved text chunks, along with the original user query, are then fed into a language model. Using this combined information, the model processes the query and relevant context to generate a coherent, contextually appropriate response.
The response is customized to address the user’s question accurately and is then delivered through a chat interface.
Difference Between RAG And Semantic Search
The table below shows the key differences between RAG and semantic search:
Why Use RAG?
1. Access to Fresh Information
LLMs are limited by the data they were trained on, which can result in outdated or inaccurate answers. RAG solves this problem by giving LLMs access to the latest information.
2. Enhanced Factual Accuracy
LLMs are great at generating creative and engaging text, but they sometimes struggle with getting facts right. This happens because they’re trained on huge amounts of text data which can include inaccuracies or biases.
Gemini’s long context window (LCW) is a useful tool for providing source material to the LLM. If you need to include more information than the LCW can handle, or if you want to scale up performance, using a RAG approach can help by reducing token usage, saving you both time and cost.
3. Searching with Vector Databases and Relevance Re-ranking
Today’s search engines use vector databases to quickly retrieve relevant documents. These databases store documents as "embeddings" in a high-dimensional space.
Multi-modal embeddings can also be used for images, audio, video, and other media types. These media embeddings can be retrieved alongside text or even in multiple languages.
4. Boosting Accuracy, Relevance, and Quality
The retrieval process in RAG is crucial. You need a strong semantic search and a curated knowledge base to ensure the retrieved information matches the input query.
If the information is irrelevant, the generated text could be off-topic or incorrect. By fine-tuning or adjusting prompts, RAG ensures the text is based on the retrieved knowledge.
The Vertex Eval Service evaluates LLM-generated text and retrieved chunks using metrics like coherence, fluency, groundedness, safety, and question-answering quality.
5. RAGs, Virtual Agents, and Chatbots
RAG and grounding can be added to any LLM application or agent that needs access to up-to-date, private, or specialized data. By using external information, RAG-powered chatbots, and agents can give more detailed, relevant, and context-aware responses.
Frequently Asked Questions
1. What is the main purpose of RAG?
The main purpose of RAG (Retrieval-Augmented Generation) is to enhance language models by combining external information with their responses. It retrieves relevant data from documents or databases and uses it to generate more accurate and contextually relevant answers.
2. Does ChatGPT have RAG?
Yes, ChatGPT can be integrated with RAG (Retrieval-Augmented Generation) systems. By connecting it to external databases or document retrieval tools, ChatGPT can access and use additional information to enhance its responses.
3. What is the difference between RAG and LLM agents?
RAG focuses on improving responses by retrieving and using external data, while LLM agents are designed to perform tasks, make decisions, or interact with systems. RAG is for better answers, and LLM agents are like smart assistants for broader tasks.
4. What is LLM and RAG?
LLM (Large Language Model) is an AI system trained to understand and generate human-like text. RAG (Retrieval-Augmented Generation) is a method that combines LLM with a retrieval system to find and use external information.
Final Words
RAG (Retrieval-Augmented Generation) revolutionizes AI by combining language models with real-time data retrieval. It enhances decision-making, improves customer experiences, and streamlines processes, making AI tools more reliable and efficient. With its versatility and benefits, RAG is shaping the future of smarter, more responsive AI systems.
