Retrieval Augmented Generation: What is it and Why do we need it?

What is Retrieval Augmented Generation?

Generative AI is currently garnering lots of attention. While the responses provided by the large language models (LLMs) are satisfactory in most situations, sometimes we want to get better focused responses when employing LLMs in specific domains. Retrieval-augmented generation (RAG) offers one such way to improve the output of generative AI systems. RAG enhances the LLMs capabilities by providing them with additional knowledge context through information retrieval. Thus, RAG aims to combine the strengths of both retrieval-based methods, which focus on selecting relevant information, and generation-based methods, which produce coherent and fluent text. 

RAG works in the following way:

1. Retrieval: The process starts with retrieving relevant documents, passages, or pieces of information from a pre-defined corpus or database. These retrieved sources contain content that is related to the topic or context for which you want to generate text.
2. Generation: After retrieving the relevant content, the generation step takes over. It involves using the retrieved information as input or context to guide the generation of coherent and contextually relevant text. This can involve techniques such as fine-tuning large language models like GPT-3 on the retrieved content or using it as a prompt.
3. Combination: The generated text is produced while taking into consideration both the retrieved information and the language model's inherent creative abilities. This allows the generated text to be more informative, accurate, and contextually appropriate.

How is RAG Useful?

Retrieval-augmented generation is useful for several reasons:

1. Content Quality: By incorporating information from retrieved sources, the generated text can be more accurate, relevant, and factually sound. This is particularly important for applications where accuracy and credibility are crucial.
2. Data Augmentation: Retrieval-augmented generation can be used to expand the dataset for fine-tuning language models. By combining the model's generative capabilities with real-world information, it can learn to produce more contextually relevant and diverse text.
3. Expertise Integration: In domains that require domain-specific knowledge or expertise, retrieval-augmented generation can ensure that the generated content aligns with expert knowledge.
4. Abstractive Summarization: When generating summaries, retrieval-augmented approaches can help ensure that the generated summary captures the most important and relevant information from the source documents.
5. Question Answering: In question answering tasks, retrieval-augmented generation can improve the accuracy of generated answers by incorporating relevant information from a corpus of documents.
6. Content Personalization: For chatbots and content generation systems, retrieval-augmented generation can enable more personalized and contextually relevant responses by incorporating information retrieved from a user's history or relevant documents.

The success of the RAG approach greatly depends upon how semantically close are the retrieved documents to help the generative AI system when it is responding to a user request. Retrieving meaningful chunks of text is done by nearest neighbor search implemented in a vector database with text being represented by word embeddings. Look for my next post to learn about this aspect of RAG implementation.

It's important to note that retrieval-augmented generation is a research-intensive area and involves challenges such as selecting the right retrieval sources, managing biases in retrieved content, and effectively integrating retrieved information with the language model's creative capabilities. However, it holds promise for improving the quality and utility of generated text across various NLP applications.