Introduction to Retrieval Augmented Generation

A Brief History

In the early days of natural language generation, systems primarily relied on simple rule-based approaches or templates to produce text, such as ELIZA [1]. These methods were rigid and lacked the ability to dynamically incorporate external knowledge. While they could generate grammatically correct responses, their usefulness in complex tasks was limited, as they struggled to produce relevant and accurate information on diverse topics.

ELIZA [1], the earliest chatbot

The development of large language models (LLMs), like OpenAI's GPT family, marked a significant improvement in text generation. These models, trained on vast amounts of text data, could generate coherent, contextually appropriate text on a wide variety of subjects. However, they still faced limitations: while they excelled at generating language, they had no built-in mechanism for retrieving specific, real-time information. For example, a user might ask an LLM a factual question, and the model could generate an answer, but without access to up-to-date data, the response could be inaccurate or outdated.

Retrieval-augmented generation (RAG), first published in Lewis et al. [2], was an attempt to solve this problem. By combining the capabilities of language models with external knowledge retrieval systems, RAG enables models to generate text that is not only fluent and contextually relevant but also factually grounded in real-time information.

What is retrieval-augmented generation?

Retrieval-augmented generation is a hybrid approach that enhances the power of large language models by allowing them to retrieve external knowledge dynamically while generating text. In a RAG system, when a user inputs a query, the model doesn't just rely on its internal knowledge (which is fixed based on the data it was trained on); instead, it first retrieves relevant information from external sources such as documents, databases, or APIs. This retrieved data is then integrated with the model's generative abilities to produce a response that is both informed by external facts and fluent in its expression.

The process typically involves two main components:

Retrieval Mechanism: When a query is received, the system retrieves relevant information from a pre-defined corpus or an online knowledge base. This retrieval step can be done using traditional keyword-based methods or more advanced approaches like embedding search. The goal is to find documents or pieces of information that are highly relevant to the query.

Generative Model: Once the relevant information is retrieved, the generative model (often a large transformer model like GPT or BERT) takes over. It uses both the original query and the retrieved data to generate a cohesive, contextually appropriate response. This ensures that the response is grounded in up-to-date, relevant knowledge while still maintaining the natural fluency of a purely generative model.

How does retrieval-augmented generation work?

The success of RAG lies in its ability to retrieve external knowledge in real time and synthesize it with the generative model's capabilities. Here's how the process works step by step:

Query Input: The user provides a query, such as "What are the latest advancements in quantum computing?"

Retrieval Stage: The system uses a retrieval model to search through a knowledge base or the web for relevant documents or information. This step often leverages embedding search to capture the semantic meaning of the query and retrieve information that is contextually relevant rather than just matching keywords.

Fusion of Information: The retrieved information is passed to the generative model, which then uses this external data, along with its internal knowledge, to generate a response. The fusion of retrieved data and the model's generative abilities enables the system to produce a more accurate and fact-based answer.

Response Generation: The final response, informed by both the retrieved data and the generative model’s capabilities, is presented to the user.

This integration allows for more robust and informed responses, especially for queries that require up-to-date information or specialized knowledge that a standalone language model might not be able to generate correctly.

Retrieval-augmented generation process [2]

Practical applications of retrieval-augmented generation

RAG systems have seen growing adoption across industries due to their ability to combine the best of both worlds: fluent language generation and real-time information retrieval.

One prominent application of RAG is in customer support systems. By using a RAG approach, these systems can generate helpful, context-specific responses to customer queries by retrieving relevant support documentation or articles. For example, if a customer asks, "How do I reset my router?" the RAG model can retrieve the latest troubleshooting guide from a knowledge base and generate a detailed, accurate response based on that information.

Another key area where RAG is making an impact is in research and content creation. Writers and researchers can use RAG-powered tools to generate content that is grounded in the most current information. For instance, a writer working on a scientific article could use a RAG system to retrieve the latest research papers and incorporate them into a well-written summary. This minimizes the need to manually search for relevant sources and allows for a more seamless content creation process.

RAG also plays a critical role in search engines. Unlike traditional search engines that return a list of documents, RAG systems can generate a summary or response that synthesizes information from multiple sources. This provides a more user-friendly experience, allowing users to get concise, relevant answers without having to sift through multiple pages.

Using Retrieval-Augmented Generation in TrueState

On the TrueState platform, we provide an easy to use and rapid no-code tooling to allow you to quickly spin up an RAG system for your dataset.

Retrieval-augmented generation mechanism in TrueState

To implement RAG within TrueState, developers can use the Embeddings Workflow Template to apply an embedding to their dataset. Then, by creating a Dashboard for the dataset, they can access the retrieval-augmented generation function and talk to their dataset aware chatbot, generating responses that are both informed by external knowledge and contextually appropriate. The Embeddings Tutorial and Dashboard Tutorial provide detailed instructions on how to set up and use this functionality.

Dashboard for retrieval-augmented generation in TrueState

References

1. Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.
2. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2021). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv preprint arXiv:2005.11401.