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RAG: Overcoming the limitations of large language models in AI systems

Leveraging Large Language Models (LLMs) in our work has never been easier. These are incredibly powerful and genuinely open up a whole world of enhancements we can make to the applications that we’re building. While these models are fantastic at communicating to us like humans in natural language, they are typically a bit behind in their knowledge of the world. So, let’s say I’m working on a chatbot that allows users to ask questions about my company's services. I’ll get answers that seem to be from another person, but will they be factually relevant or correct? Probably not. At best, the answers will be dated, and more than likely, the model won’t have any knowledge of my services at all. This common problem can be solved by leveraging RAG.

Retrieval Augmented Generation (RAG) is a powerful technique that can significantly enhance the performance of AI systems that rely on generating accurate and informative responses. By integrating an information retrieval component, RAG allows your application to leverage external knowledge, resulting in more reliable and contextually appropriate outputs. Whether you're building chatbots, virtual assistants, content generation systems, or even search engines, considering RAG as part of your architecture can take your application to the next level.

In chatbots and virtual assistants, RAG enables more engaging and informative conversations by allowing the AI to draw upon relevant external knowledge to provide accurate and specific responses. Similarly, RAG can help produce more coherent and contextually appropriate content in content generation systems by incorporating relevant information from reliable sources.

RAG can also enhance search systems by leveraging the power of language models to grasp the semantic meaning and context of the user's query, going beyond simple keyword matching. This is already a nice win, but we can take it further. With RAG, we can layer on semantic understanding with relevant knowledge retrieved from an external source. This search system provides results that are more closely aligned with the user's needs and context. This contextual relevance is a key selling point for RAG-enhanced search systems, as it significantly improves the user experience and satisfaction by delivering more targeted and relevant information.

Here's how RAG can benefit AI systems

Improved Accuracy

RAG delivers more accurate responses by leveraging external knowledge to supplement the AI's understanding, reducing the likelihood of generating incorrect or irrelevant information.

Contextual Relevance

By incorporating relevant information from external sources, RAG enables AI systems to provide more contextually appropriate responses tailored to the user's specific needs and intent.

Enhanced User Experience

With more accurate, informative, and relevant outputs, RAG can significantly improve the user experience across various AI applications, increasing user satisfaction and engagement.

Technical Approach

At a technical level, RAG introduces an information retrieval mechanism that fetches relevant data from an external knowledge source to augment a pre-trained large language model (LLM). We typically build an automated system that does this work for us. It can take many different shapes depending on the data and its source, but the idea is that we want this additional information as up-to-date as possible.

The LLM itself remains unchanged, and there's no need for additional training. Instead, RAG provides a way to incorporate up-to-date and domain-specific information at inference time dynamically. The retrieved information is then incorporated into the input prompt fed into that LLM, allowing the model to attend to both the original query and the retrieved augmentation to generate the final output text.

How Might This Work?

A background job can be set up to run at a pre-defined cadence, processing external data sources to create embeddings. An embedding is a numerical representation of data, such as text or images, in a high-dimensional space. This representation captures the semantic meaning and relationships of the data, enabling efficient comparison and retrieval based on similarity. We capture these embeddings in a database so that we can reference them for comparison later on. It sounds complicated (to be fair, it kind of is), but when you boil it down, this allows us to connect relevant bits of information in a much more sophisticated way than a keyword search.

When users interact with the system, their questions or input are converted into an embedding using the same technique used for external data.

To find the most relevant information, the user’s input embedding is compared against the pre-computed embeddings of the external data. This comparison is what starts to connect relevant information together for us.

The retrieved information is then incorporated into the input prompt and fed into the pre-trained language model (LLM). The LLM attends to the user's input and the retrieved augmentation to generate the final output text.

By leveraging this architecture, the AI system can dynamically retrieve relevant information based on the user's input and provide more accurate and contextually appropriate responses. The pre-computed embeddings of the external data enable efficient retrieval. At the same time, the LLM's ability to attend to both the user's input and the retrieved information allows for generating coherent and informative outputs.

What we end up with are better, more valuable responses from the model. You might even notice that the model is discovering connections between disparate pieces of information that you never noticed before. Perhaps a connection was made between a paragraph in a PDF white paper you published on the current state of digital transformations, the Services page on your website, and your internal database of customer service calls, which brings to your attention a completely new service line you might consider adding to address pressing customer needs. You can see how powerful this can become.

About Those External Sources

Integrating RAG into an AI system requires careful consideration of the retrieval mechanism, the quality of the external knowledge base, and the overall system's efficiency. The better and more organized the external source data is, the more helpful it will be in a system like this. This typically requires a bit of work to get the data into a solid structure and format. Sometimes this involves processing and parsing PDFs. Sometimes we need to scrape a website. Sometimes there’s a more complicated data pipeline that needs to be set up. Wherever we end up, you should account for some effort here. It’s unlikely that all your data sources will be ready to go straight away.

While this does seem like a lot of work, it’s a valuable investment. The potential benefits of improved accuracy, contextual relevance, and in the end, user satisfaction make it an effective choice for organizations looking to take their AI capabilities to the next level.

An Important Piece to Your AI System

Retrieval-augmented generation (RAG) is a powerful technique that can significantly enhance the performance of various AI-powered applications, including chatbots, virtual assistants, content generation systems, and search engines. By integrating an information retrieval component, RAG allows your application to leverage external knowledge, resulting in more accurate, relevant, and informative outputs. Considering RAG as part of your architecture is worth exploring if you’re looking to overcome the limitations of language models and supercharge your AI system.

If you have any further questions about RAG or need assistance in evaluating its potential for your specific use case, our team is here to provide guidance and support in incorporating RAG into your AI-powered applications.

Published by Matt Reich in AI

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