Understanding The Working Mechanism Behind Rag Systems

By Author: Lyra Bennett
Total Articles: 13
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Artificial Intelligence has evolved drastically over the past decade. If you’re using AI tools to ease your day-to-day work, you may use LLMs (Large Language Models). These LLM models generate results based on the pre-trained data. But even the smartest LLMs struggle with accuracy, up-to-date information, or domain-specific knowledge. Sometimes it hallucinates information that doesn’t even exist.

This paves the way for the development of Retrival Augmented Generation (RAG), which retrieves relevant documents from a knowledge base and uses them to generate more reliable, accurate outputs. But how do they do it effortlessly? In this article, let’s explore how RAG works to produce the result.

How does a RAG System Work?
Below is a step-by-step breakdown of the working of the RAG system:

User Enters a Query
The working process of RAG systems begins when a user enters a query. Let's say the user types “How to set an ATM pin online?” The RAG doesn’t generate text instantly. Instead, it reads, understands, and prepares the question to go through a structured pipeline.

Converting ...
... The Query Into Embedding
The system converts the question into a numerical vector called an embedding using embedding models. The embedding is nothing but a vector, which is a list of numbers that represent the meaning of the query. The system converts the text into a vector to match it with relevant information.

Retrieval From a Vector Database
The embedding is used to search a vector database like Pinecone, Weaviate, or FAISS. The vector database contains thousands or millions of embeddings. The system finds documents whose embedding is similar to the query embedding. The system returns similar snippets, such as a section, paragraph, or blog post summary, to the given query.

Constructing The Final Prompt
The system collects the user’s original question, the retrieved documents, and additional instructions or safety prompts, and sends them to the language model. This gives the language model a context-rich prompt to work with.

The Model Generates The Answer
The system has an LLM to generate the result. The LLM uses both the external information and its internal reasoning to produce a grounded, accurate response. Thus, the model generates answers by referencing real documents and reducing hallucinations.

Importance of the RAG system
The RAG system is becoming essential to produce accurate, reliable, and up-to-date information. Here is why it is important:
It produces accurate results using real data.
It reduces hallucinations, which is essential for critical tasks.
It easily handles domain-specific content like manuals, PDFs, and policies.
It stays up-to-date as it keeps updating its knowledge base.

Conclusion
Retrieval Augmented Generation is becoming the most important innovation in modern AI. It generates factual, relevant, and reliable information by combining a search engine’s precision with a language model’s fluency. If you’re considering implementing RAG in your business, choose the prominent RAG development company that builds a custom RAG solution tailored to your data, workflows, and goals.