By grounding AI in an organization's unique expertise, Retrieval-Augmented Generation (RAG) helps enterprises overcome hurdles in deploying large language models. AI21's RAG Engine provides advanced retrieval capabilities without enterprises having to invest heavily in development and maintenance.
If you’ve been following generative AI and large language models in the past few months, chances are you have also heard the term Retrieval-Augmented Generation or RAG for short. Not just a buzzword, RAG shows incredible promise in overcoming hurdles in large language models (LLMs) that currently prevent adoption for enterprises in production environments.
For LLMs like Jurassic to truly solve a business problem, they need to be attuned to the unique body of knowledge that each organization has. Think of a generative AI-powered chatbot that interacts with retail bank customers. A bot powered by a general knowledge-trained LLM can broadly inform customers what a mortgage is and when it can generally be issued, but this is hardly helpful to a customer who wants to know how a mortgage is applicable to their specific circumstance. On the other hand, a chatbot using RAG understands the context: the bank’s unique mortgage policies, customer banking details, and other proprietary organizational information to provide a tailored, accurate, grounded answer to a customer’s question about a mortgage.
Retrieval Augmented Generation (RAG) integrates the deep understanding and generation capabilities that language models have, with the vast, unique knowledge of each organization. It does this by combining two processes: retrieval and augmented generation. Retrieval involves searching through documents to find relevant information that matches a user’s query or input. Augmented generation then generates text based on the retrieved information, using instruction-following large language models (LLMs) or task-specific models.
Companies across industries are experimenting with implementing RAG into their systems, recognizing its potential to significantly enhance the quality and relevance of generated content by providing up-to-date, factual information drawn from a broad range of sources within the organization. RAG gives organizations the ability to base text generation on information contained in a corpus of text, also known as grounding.
By retrieving relevant context using RAG, companies can realize many benefits in their generative AI solutions, such as:
RAG use cases in applications can generally be categorized into two main categories: Internal (organizational-facing) applications that are aimed at improving organizational efficiency and knowledge management and External (customer-facing) applications which focus on enhancing customer experience and engagement.
Internal RAG-based applications target internal stakeholders within an organization, such as employees or managers, helping them navigate and utilize the vast amount of organizational knowledge effectively. Below are just a few examples of use cases we’ve seen our customers adopt.
External RAG-based applications focus on enhancing the customer experience and engagement, retrieving secured organizational data on behalf of customers or clients.
As its name implies, RAG consists of two main steps, retrieval and augmented generation.
Retrieval is the process of searching through organizational documents to find relevant information that matches a user's query or input. Retrieval techniques range from simple keyword matching to more complex algorithms that analyze document relevance and user context. The effectiveness of a retrieval system is measured by its ability to provide accurate, relevant, and timely information, meeting the precise needs of its users.
One of the more advanced approaches on the retrieval spectrum is semantic search. Semantic search is the technique of understanding the underlying intent of a search query to retrieve relevant results from a corpus of documents. Beyond simple keyword matching, it matches the semantic meaning with the help of machine learning and AI technologies. For example, semantic search would know to closely match the terms “cute kittens” to “fluffy felines”, even though there is no literal word match.
There are several steps required to build a semantic search system, involving different processes:
Once organizational information is retrieved through semantic search, general purpose LLMs like Jurassic can then generate a response according to the prompt it was given (such as “summarize the content”, or “answer an end user question”). Task-specific models such as Contextual Answers, designed for RAG-based Q&A, can also be used out-of-the-box to craft an answer to a question without any prompt engineering needed.
Although retrieval tools and knowledge are widely available, moving from proof of concept (POC) to production for enterprises is harder than it seems. No matter how tech savvy your organization may be, building and maintaining a multi-document retrieval system is complex and comes with many difficulties:
While individual tools for creating retrieval solutions are becoming more accessible and various new retrieval frameworks are emerging, developing a robust semantic search system remains a significant challenge for organizations.
AI21's RAG Engine offers enterprises an all-in-one solution for implementing Retrieval-Augmented Generation. RAG Engine allows companies to upload their organizational documents, retrieve the most relevant information for a given query, and connect that context to a large language model like Jurassic-2 or a task-specific model to generate text. RAG Engine is conveniently packaged and accessible via an API endpoint.
It also adeptly addresses implementation challenges, offering a RAG solution built for production use cases in the enterprise. It lets you efficiently integrate advanced retrieval capabilities without having to invest heavily in development and maintenance. RAG Engine contains built-in mechanisms for every step of the way, including document extraction, chunking, embeddings, vector storage and retrieval.
We’ve used our experience helping thousands of global enterprises, including leaders in banking, life sciences and retail, to create the optimal retrieval solution. We believe organizations can greatly benefit from out-of-the-box solutions that streamline the process and reduce technical overhead so they can focus on their core business.
RAG Engine gives enterprises a robust managed retrieval system that integrates production-grade models as key components. However, if you prefer the flexibility to build your own retrieval solution, you can access these task-specific models, optimized to excel in their respective task.
Many enterprises are looking to move beyond LLM experimentation to adoption by implementing RAG-based solutions. RAG holds a lot of promise for overcoming reliability challenges through grounding and deep understanding of a given context. Despite the plethora and availability of knowledge and tools, building a RAG system fit for enterprise production needs is not as simple as it seems. Organizations must build, optimize and continuously maintain numerous processes of the RAG pipeline, including chunking and embedding, in order to produce an optimal context that can be integrated with LLM generation capabilities.
No matter how technologically adept your organization is, building a RAG solution is costly in time and resources. With customers from the top banks, analytics, healthcare and retail companies utilizing our RAG Engine, we can help.
