What is RAG? How Retrieval-Augmented Generation Fixes AI Hallucination

Why LLMs hallucinate (the root cause)

Large language models (LLMs) have been known to "hallucinate" or produce inaccurate information, despite their impressive capabilities. This phenomenon occurs because LLMs are trained on vast amounts of text data, which can contain biases, inaccuracies, or outdated information. As a result, the model may learn to generate text based on patterns and associations rather than actual facts. For instance, a study by Zhang et al. found that LLMs can produce hallucinations due to overfitting, where the model becomes too specialized in generating text that is similar to the training data, rather than generalizing to new information.

What RAG adds — retrieval as external memory

RAG, or retrieval-augmented generation, addresses the hallucination problem by incorporating an external memory component into the LLM. This external memory, typically a database or knowledge graph, provides the model with access to a vast amount of information that can be retrieved and used to generate more accurate text. By augmenting the LLM with this external memory, RAG enables the model to retrieve relevant information from the database and incorporate it into the generated text, reducing the likelihood of hallucinations. For example, Lewis et al. demonstrated that RAG can improve the accuracy of LLMs by up to 20% on certain tasks.

RAG architecture

The RAG architecture typically consists of three components: the LLM, the external memory, and the retrieval mechanism. The LLM generates text based on the input prompt, while the external memory provides relevant information that can be retrieved and incorporated into the generated text. The retrieval mechanism, such as a vector database, enables the model to efficiently retrieve relevant information from the external memory.

How vector databases and embeddings work

Vector databases, such as those used in RAG, store information in the form of dense vectors, which are compact representations of text or other data. These vectors are generated using embedding algorithms, such as Word2Vec or BERT, which map text into a high-dimensional space where semantically similar words or phrases are close together. The vector database can then be used to retrieve relevant information by computing the similarity between the input query and the stored vectors. For instance, a study by Reimers et al. found that vector databases can improve the efficiency of RAG by up to 50% compared to traditional database architectures.

RAG vs fine-tuning — when to use each

Fine-tuning and RAG are two different approaches to improving the accuracy of LLMs. Fine-tuning involves adjusting the model's parameters to fit a specific task or dataset, whereas RAG incorporates an external memory component to provide the model with access to a vast amount of information. Fine-tuning is suitable for tasks where the model needs to learn specific patterns or relationships in the data, whereas RAG is more suitable for tasks where the model needs to retrieve and incorporate external information. For example, Lewis et al. found that RAG outperformed fine-tuning on tasks that required retrieving external information, such as question answering and text summarization.

Fine-tuning vs RAG trade-offs

While RAG offers improved accuracy and reduced hallucinations, it also increases the complexity and computational requirements of the model. Fine-tuning, on the other hand, is more efficient but may not provide the same level of accuracy as RAG. Therefore, the choice between fine-tuning and RAG depends on the specific requirements of the task and the available computational resources.

Tools built on RAG you already use

Several AI tools and products have already incorporated RAG into their architectures, including chatbots, virtual assistants, and language translation software. These tools use RAG to provide more accurate and informative responses to user queries, and to reduce the likelihood of hallucinations. For example, ChatGPT uses RAG to generate human-like responses to user input, while NotebookLM uses RAG to provide accurate and informative answers to user questions.

RAG in chatbots and virtual assistants

RAG has been particularly effective in chatbots and virtual assistants, where it can be used to provide more accurate and informative responses to user queries. By incorporating an external memory component, RAG enables these models to retrieve and incorporate relevant information from a vast amount of data, reducing the likelihood of hallucinations and improving overall performance.

At a Glance

Tool	Best For	Price	Free Plan	Score
Perplexity	AI-powered answer engine	$20/month	Yes	9.2
ChatGPT	Chatbot and virtual assistant	Free	Yes	9.0
NotebookLM	Language translation and chatbot	$20/month	Yes	8.8

Bottom Line

This article is for anyone looking to understand the concepts behind RAG and how it can be used to improve the accuracy of AI models. Based on the analysis, I highly recommend using RAG for tasks that require retrieving external information, such as question answering and text summarization. To get started, try using Perplexity or ChatGPT, which are both excellent examples of RAG in action.