What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.
Why is Retrieval-Augmented Generation important?
LLMs are a key artificial intelligence (AI) technology powering intelligent chatbots and other natural language processing (NLP) applications. The goal is to create bots that can answer user questions in various contexts by cross-referencing authoritative knowledge sources.
Unfortunately, the nature of LLM technology introduces unpredictability in LLM responses. Additionally, LLM training data is static and introduces a cut-off date on the knowledge it has. Implementing RAG in an LLM-based question answering system has two main benefits: It ensures that the model has access to the most current, reliable facts, and that users have access to the model’s sources, ensuring that its claims can be checked for accuracy and ultimately trusted.
Known challenges of LLMs include:
* Presenting false information when it does not have the answer.
* Presenting out-of-date or generic information when the user expects a specific, current response.
* Creating a response from non-authoritative sources.
* Creating inaccurate responses due to terminology confusion, wherein different training sources use the same terminology to talk about different things.
You can think of the Large Language Model as an over-enthusiastic new employee who refuses to stay informed with current events but will always answer every question with absolute confidence. Unfortunately, such an attitude can negatively impact user trust and is not something you want your chatbots to emulate!
RAG is one approach to solving some of these challenges. It redirects the LLM to retrieve relevant information from authoritative, pre-determined knowledge sources. Organizations have greater control over the generated text output, and users gain insights into how the LLM generates the response.
What are the benefits of Retrieval-Augmented Generation?
RAG technology brings several benefits to an organization's generative AI efforts.
Cost-effective implementation
Chatbot development typically begins using a foundation model. Foundation models (FMs) are API-accessible LLMs trained on a broad spectrum of generalized and unlabeled data. The computational and financial costs of retraining FMs for organization or domain-specific information are high. RAG is a more cost-effective approach to introducing new data to the LLM. It makes generative artificial intelligence (generative AI) technology more broadly accessible and usable.
Current information
Even if the original training data sources for an LLM are suitable for your needs, it is challenging to maintain relevancy. RAG allows developers to provide the latest research, statistics, or news to the generative models. They can use RAG to connect the LLM directly to live social media feeds, news sites, or other frequently-updated information sources. The LLM can then provide the latest information to the users.
RAG has additional benefits. By grounding an LLM on a set of external, verifiable facts, the model has fewer opportunities to pull information baked into its parameters. This reduces the chances that an LLM will leak sensitive data, or ‘hallucinate’ incorrect or misleading information.
RAG also reduces the need for users to continuously train the model on new data and update its parameters as circumstances evolve. In this way, RAG can lower the computational and financial costs of running LLM-powered chatbots in an enterprise setting.
Enhanced user trust
RAG allows the LLM to present accurate information with source attribution. The output can include citations or references to sources. Users can also look up source documents themselves if they require further clarification or more detail. This can increase trust and confidence in your generative AI solution.
More developer control
With RAG, developers can test and improve their chat applications more efficiently. They can control and change the LLM's information sources to adapt to changing requirements or cross-functional usage. Developers can also restrict sensitive information retrieval to different authorization levels and ensure the LLM generates appropriate responses. In addition, they can also troubleshoot and make fixes if the LLM references incorrect information sources for specific questions. Organizations can implement generative AI technology more confidently for a broader range of applications.
How does Retrieval-Augmented Generation work?
Without RAG, the LLM takes the user input and creates a response based on information it was trained on—or what it already knows. With RAG, an information retrieval component is introduced that utilizes the user input to first pull information from a new data source. The user query and the relevant information are both given to the LLM. The LLM uses the new knowledge and its training data to create better responses. The following sections provide an overview of the process.
Adding an information retrieval system gives you control over grounding data used by an LLM when it formulates a response. For an enterprise solution, RAG architecture means that you can constrain generative AI to your enterprise content sourced from vectorized documents and images, and other data formats if you have embedding models for that content.
The decision about which information retrieval system to use is critical because it determines the inputs to the LLM. The information retrieval system should provide:
* Indexing strategies that load and refresh at scale, for all of your content, at the frequency you require.
* Query capabilities and relevance tuning. The system should return relevant results, in the short-form formats necessary for meeting the token length requirements of LLM inputs.
* Security, global reach, and reliability for both data and operations.
* Integration with embedding models for indexing, and chat models or language understanding models for retrieval.
Summary
Underpinning all foundation models, including LLMs, is an AI architecture known as the transformer. It turns heaps of raw data into a compressed representation of its basic structure. Starting from this raw representation, a foundation model can be adapted to a variety of tasks with some additional fine-tuning on labeled, domain-specific knowledge. But fine-tuning alone rarely gives the model the full breadth of knowledge it needs to answer highly specific questions in an ever-changing context.
RAG is currently the best-known tool for grounding LLMs on the latest, verifiable information, and lowering the costs of having to constantly retrain and update them. RAG depends on the ability to enrich prompts with relevant information contained in vectors, which are mathematical representations of data. Vector databases can efficiently index, store and retrieve information for things like recommendation engines and chatbots.
Comments