What Is Retrieval-Augmented Generation?

There’s no doubt that large language models (LLMs) have transformed natural language processing, but at times, they can be inconsistent, random, or even plain wrong in the responses they deliver to a prompt. While this can certainly lead to some laughter, it’s not ideal when you’re relying on LLMs for accurate and verifiable information.

Many technical teams are working on improving the accuracy of large language models. One method that has emerged in response to this endeavor is retrieval-augmented generation (RAG). Coined by a group of individuals from the Fundamental Artificial Intelligence Research (FAIR) team, University College London (UCL), and New York University (NYU), retrieval-augmented generation (RAG) refers to a technique that aids the accuracy of large language models by enabling the model to have access to external facts.

How Does RAG Work? 

Typically, large language models (LLMs) take a user’s input and deliver responses based on information the LLM has been trained on (which can sometimes be outdated or incorrect). RAG combines this information with supplemental data like a company’s knowledge base or relevant documents, enabling it to deliver factually accurate, contextually relevant responses.

Semantic Search vs. RAG

Semantic search delivers relevant results using natural language processing to understand the intent behind a user’s query.  However, semantic search engines are only as good as the data and algorithms they’re trained on. 

As mentioned above, RAG is so effective because it uses LLM retrieval and generation techniques and incorporates trusted external sources outside of its training data to generate relevant, accurate responses. 

RAG Use Cases

Retrieval-augmented generation has many use cases. Some examples of these include:

Building a Q&A System

RAG enables users to input questions and receive detailed, relevant answers. Compared to traditional Q&A models or systems, RAG can provide higher accuracy and more in-depth knowledge.

Conversational Systems

When building chatbots, RAG can aid in providing a variety of informational and relevant responses to user inquiries, especially when conversations cover multiple topics or require access to large amounts of information. Consider an insurance chatbot. These chatbots should be able to answer questions ranging from onboarding to claims processing, in addition to providing many other kinds of customer support. 

Educational Systems

RAG can be utilized in various educational systems. Not only can it provide answers to questions, but it can also provide background information on how to arrive at answers and create learning material based on students’ questions. RAG can enhance the learning experience for students from kindergarten through college and beyond. 

Content and Report Generation

RAG can assist in creating reports based on relevant information and even aid in content generation, such as articles, social media posts, and video scripts. Using RAG for these materials can cut research and brainstorming time for content creators and increase their output. 

How to Implement RAG

Implementing RAG involves the following steps: 

    1. Start with a Pre-Trained Language Model

The first thing you need to do is choose a pre-trained language model. These models have been trained on various data and can generate coherent and relevant text (albeit not always up-to-date or entirely accurate). There are also libraries online that enable developers to easily access and use pre-trained language models (for example, Hugging Face’s Transformers). 

    1. Document Retrieval

Next, you should implement a retrieval system to retrieve relevant documents based on user input. There’s an option to build or use a variety of documents relevant to your industry or task. Alternatively, there are more traditional methods, such as using Okapi BM25 or Term Frequency-Inverse Document Frequency (TF-IDF), or neural retrieval models, such as Dense Passage Retrieval (DPR).

    1. Contextual Embedding

Contextual embeddings help identify the true sentiment of a word based on the surrounding text, which helps provide a better representation than traditional word embeddings. Contextual embedding can be obtained using models like Bidirectional Encoder Representations from Transformers (BERT for short). 

    1. Combination (Concatenation)

Once you’ve utilized the contextual embeddings, you’ll need to combine them with context. You can do this by combining the embeddings of the input with the embeddings of the documents or by using attention mechanisms to weigh the importance of each document’s embeddings based on the context of the input.

    1. Fine-Tuning

Fine-tuning is optional but can improve the model’s performance. You can use fine-tuning to speed up training, tackle specific use cases, and improve user experience. 

    1. Inference

This last step will feed the context into the model and retrieve the relevant documents using the document retrieval system. It will also combine the input embeddings with the document embeddings and generate a response using the combined model.

Luckily, there are libraries out there that provide pre-trained tools for implementing RAG-like systems, making this entire process easier and more accessible to developers.

Benefits of Retrieval-Augmented Generation

One of the biggest benefits of retrieval-augmented generation is the improved quality and relevance of the generated responses due to the large language model having access to more accurate and relevant information than it would have otherwise. 

Another benefit is RAG’s ability to provide domain-specific information. Because you can fine-tune RAG models for specific tasks or use cases, they can benefit users by providing information unique to their situation. 

Since RAG not only retrieves relevant information but also generates a natural response, interactions with these models will generally be more conversational and user-friendly.

Key Takeaways and Additional Resources

Retrieval-augmented generation offers an improved version of traditional large language models by combining the strengths of LLMs with external access to accurate, up-to-date information.  

To continue learning about topics related to retrieval-augmented generation, check out these resources:

Author

Posted by Caroline Kerns

Caroline Kerns is a Developer Community Manager at Couchbase with a decade of community management experience in the tech industry. She loves connecting people and has worked on various teams, where she enjoys fostering collaboration and building strong communities.

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