Retrieval-augmented generation (RAG) is an AI framework designed to enhance the quality of responses generated by large language models (LLM). It leverages external knowledge sources, typically knowledge graphs or other knowledge bases, such as Wikipedia, to augment the generation capabilities of these models. This integration enables the model to incorporate additional external information during text generation, thereby improving its accuracy and applicability while ensuring access to the latest reliable information.

The retrieval-augmented generator (RAG) was initially introduced in “REALM: Retrieval-Augmented Language Model Pretraining” by Guu et al. (2020), where they discuss the utilization of dense retrievers to enhance language model pre-training.

Response

RAG can be likened to a detective and storyteller duo. Imagine you are trying to solve a complex mystery. The detective's role is to gather clues, evidence, and historical records related to the case. Once the detective has compiled this information, the storyteller designs a compelling narrative that weaves together the facts and presents a coherent story. In the context of AI, RAG operates similarly.

The Retriever Component acts as the detective, scouring databases, documents, and knowledge sources for relevant information and evidence. It compiles a comprehensive set of facts and data points.

The Generator Component assumes the role of the storyteller. Taking the collected information and transforming it into a coherent and engaging narrative, presenting a clear and detailed account of the mystery, much like a detective novel author.

RAG consists of two distinct phases: retrieval and content generation. In the retrieval phase, algorithms search for and retrieve relevant information from external knowledge bases. This information is then used in the generative phase, where the LLM synthesizes an answer based on both the augmented prompt and its internal representation of training data.

Phase 1. Retrieval

Phase 2. Content Generation

Content Generation

Here's another example to understand this better.

I have a short story that illustrates the scene or situation, and based on that, we can create a list of questions, similar to a comprehension exercise.

Short story

We can raise out question in chat window and RAG will revert you with favorable and most relevance answer to it.

RAG

Implementation of RAG

For the above scenario, we have used Ollama’s embedding and text generation LLM model and Angular for the front end.

To understand Ollama in detail, please go through the earlier article [Introduction to Ollama] for your reference.

In the above scenario, we have been taking the short story as context and converting it into an embedding using OpenAI’s LLM model.

LLM Model

Processing the context into a vector database converts the text into numbers.

Vector Database

The same goes for the query or question we ask in chat, which gets converted into an embedding, too.

We will compare the two embeddings to find the best match, then send that context to various Ollama models for text generation, aiming to provide a proper output or response to your query.

Output

Here, we have maintained the threshold value to estimate the best score that matches the context and query embedding. Once we have figured out the relevance piece, we send it out to another Ollama’s text generation model as a prompt, which gives us the expected result.

Model

Instead of the custom hard-coded context, we can utilize data from the database or a document file to create a vector database, enabling us to find the best match for your query.

The Benefits of Retrieval-Augmented Generation

Retrieval Augmented Generation offers a range of benefits in the field of NLP and text generation.

Applications of RAG

RAG finds applications in various domains and industries, leveraging its ability to combine retrieval-based and generative techniques to enhance text generation and information retrieval. Here are some notable applications of RAG.

These applications highlight the versatility and utility of RAG in various fields, where the combination of retrieval and generation capabilities significantly enhances text-based tasks and information retrieval processes.

Conclusion

RAG is a promising approach for improving LLM accuracy and reliability, offering benefits like factual grounding, reduced bias, and lower maintenance costs. While challenges remain in areas like unknown recognition and retrieval optimization, ongoing research is pushing the boundaries of RAG capabilities and paving the way for more trustworthy and informative LLM applications.

Reference Site for more details: https://www.promptingguide.ai/research/rag