RAG Explained: A Beginner's Guide to Retrieval-Augmented Generation
If you've started learning Generative AI , you've probably come across terms like: LLM. Embeddings. Vector Search. RAG. At first, these terms can sound complicated. But the basic idea behind RAG is actually quite simple. Let's understand it with a real-world example. 👇 🤔 Imagine You Have 10,000 Company Documents Imagine your company has thousands of documents: HR policies Travel policies…
Retrieval-Augmented Generation (RAG) is a concept in Generative AI that simplifies the process of finding and using relevant information. Imagine a company with thousands of documents like HR policies, travel policies, technical guides, and more. When an employee asks a question like, "How much can I claim for a hotel during business travel?", a RAG system can help provide the correct answer.
RAG works by first finding the right information and then asking an AI model to generate an answer based on that information. Here's a simplified flow:
1. User asks a question
2. Find relevant information
3. Provide that information to the AI
4. AI generates an answer
For example, if a travel policy states that employees can claim hotel expenses up to ₹5,000 per night, an employee asking about the hotel reimbursement limit would receive a response stating, "According to the travel policy, employees can claim hotel expenses up to ₹5,000 per night." The AI didn't need to know your company's policy beforehand; the system retrieved the relevant information first.
RAG consists of two main stages:
1. Prepare the knowledge
2. Answer the user's question
In the preparation stage, we need to convert large documents into smaller pieces called chunks. For instance, a 100-page travel policy can be divided into smaller chunks, each containing relevant information. This helps the system find the specific section related to a question more efficiently.
The next step involves creating embeddings, which are numerical representations of text that help the system measure semantic similarity. Embeddings allow the system to find information based on meaning rather than just matching exact words. Popular vector databases and systems for storing the information include Azure AI Search, PostgreSQL + pgvector, Pinecone, Qdrant, and Weaviate.
When a user asks a question, the application converts it into an embedding and searches the knowledge base for relevant results. It then identifies the most relevant information and provides it to the AI model, which generates the answer. This approach allows the AI to provide accurate and context-specific answers without relying on pre-existing knowledge of the specific topic.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
