The Promise Of Rag Bringing Enterprise Generative Ai To Life

The Promise of RAG: Bringing Enterprise Generative AI to Life | AI21

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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.

What is Retrieval Augmented Generation (RAG)?

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:

Improving factual accuracy & minimizing risk

Generating more specific and nuanced text

Allowing for better personalization

Top RAG Use Cases in the Enterprise

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 use cases

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.

Employee productivity apps – Assist employees in finding and leveraging existing organizational knowledge buried across databases, emails, and documents.

Analysis assistants – Provide insights to employees through document summarization or Q&A by retrieving and synthesizing internal research documentations, such as financial reports, research studies, clinical trials, market trends, competitor analysis, and customer feedback.

Employee training tools – Help onboard and educate new employees with tools that locate relevant information.

External RAG use cases

External RAG-based applications focus on enhancing the customer experience and engagement, retrieving secured organizational data on behalf of customers or clients.

Customer support chatbots – Enhance customer support by providing accurate, context-rich responses to customer queries, based on specific user information and organizational documents like help center content & product overviews.

Public-facing Q&A systems – Address customer inquiries and tickets with accurate and detailed answers grounded in your organization’s proprietary knowledge. For example, implemented in a retail website, such a system would address questions about specific products, shipping policies, and more. An important feature is that the system won’t respond to any questions whose answers aren’t in the associated documents. This is critical for mitigating risk and ensuring compliance especially for privacy-sensitive enterprises.

How Does It Work?

As its name implies, RAG consists of two main steps, retrieval and augmented generation.

Retrieval

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.

Semantic Search

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:

Embedding (vectors): An embedding model transforms text from indexed documents and a query into fixed-size vectors (a list of numbers), capturing their semantic meanings in a high-dimensional space. This allows computers to semantically search for relevant information in a user query.

Vector Database: Embeddings are typically stored in a dedicated vector database (provided by vendors such as Pinecone or Weaviate ), which can search through vectors to find the most similar results for a user query. Vector databases are designed to be highly scalable and efficient when searching through billions of vectors.

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