The RAG-LLM Relationship

Retrieval-Augmented Generation (RAG) integrates a Large Language Model (LLM) with an organization's internal data, to generate more accurate responses from GenAI apps.    

What is RAG? 

Retrieval-augmented generation is a Generative AI (GenAI) design pattern, that augments a large language model with fresh, trusted data retrieved from authoritative internal knowledge bases and enterprise systems, to generate more informed and reliable responses. 

What is an LLM? 

A large language model is a language model trained on a vast amount of textual data, typically billions or trillions of words. By studying all this data, the model learns the intricate patterns and complex relationships that exist between words and ideas, enabling it to communicate with humans more effectively. 

RAG-LLM Challenges 

Since its conception in 2020, RAG has been based on the retrieval of documents from a company’s knowledge bases.

In a December 2023 report on conversational AI, Gartner estimates that it will take a few years for enterprises to adopt RAG due to challenges like the need to: 

1. Apply GenAI to self-service customer support mechanisms, like chatbots 

2. Build and integrating retrieval pipelines into applications

3. Combine insight engines with knowledge bases, to run the retrieval function

4. Index, embed, pre-process, and/or graph enterprise data and documents

5. Keep sensitive data hidden from people who aren’t authorized to see it 

All the above are problematic for enterprises because of data sprawl, ownership issues, skillset gaps, and technical restrictions.

In a subsequent January 2024 Gartner RAG report on augmenting large language models with internal data, enterprises are advised to:  

• Choose a pilot use case, in which business value can be clearly measured.

• Classify the use case data, as structured, semi-structured, or unstructured, to decide on the best ways of handling the data and mitigating risk.

• Get all the metadata you can, because it provides the context for your RAG deployment and the basis for selecting your enabling technologies. 

 Get the condensed version of the Gartner RAG report. 

RAG-LLM Interaction

RAGs use LLMs to execute their retrieval and generative capabilities by: 

1. Sourcing  

   The first step in any RAG solution is data sourcing, usually from internal text documents and enterprise systems. The source data is basically your company’s knowledge base that the retrieval model sifts through to locate and aggregate relevant information. To ensure accurate, diverse, and trusted data sourcing, data redundancy must be minimized. 

2. Unifying data for retrieval 

   You should organize your data and metadata in such a way that RAG can instantly access it. For example, your customer 360 data – including master data, transactional data, and interaction data – should be unified for real-time retrieval. Depending on the use case, you may have to arrange your data by other business entities, like employees, products, suppliers, or anything else that’s relevant to your use case. 

3. Chunking documents 

   For the retrieval model to work effectively on unstructured documents, divvying up the data into more manageable chunks is advisable. Proper chunking can improve retrieval performance and accuracy. For example, a document may be a chunk on its own, but it could also be chunked down further into chapters, paragraphs, sentences, or even words. 

4. Embedding (converting text to vector formats) 

   The text found in documents must be converted into a format that RAG can use for search and retrieval. This process, called embedding, might entail transforming the text into vectors that are stored in a vector database. The embeddings are linked back to the source data, leading to more accurate and meaningful responses. 

5. Protecting sensitive data 

   Unauthorized users should never be given access to the sensitive data retrieved by RAG. For example, a salesperson should never see a customer’s credit card information, and a customer service agent should never see a caller’s Social Security Number. To achieve this, your RAG solution should have dynamic data masking capabilities and role-based access controls built in.  

6. Generating the prompt  

   Your RAG solution should automatically generate an enriched prompt by creating a story out of the retrieved 360-degree data. And there needs to be an ongoing tuning process for prompt engineering, facilitated by Machine Learning (ML) models of possible. 

RAG-LLM Benefits 

By deploying RAG, enterprises benefit from: 

• More rapid and cost-effective time to value 

  Training an LLM is very time-consuming and costly. RAG makes GenAI accessible and reliable for customer-facing operations by offering a quicker and more affordable way to introduce new data to the LLM. 

• Personalization of user interactions 

  By integrating a specific 360-degree dataset with the extensive general knowledge of the LLM, RAG personalizes user interactions via chatbots, and customizes marketing insights like up-sell and cross-sell recommendations by human beings. 

• Enhanced user trust 

  RAG-powered LLMs generate reliable information via a combination of data accuracy, freshness, and relevance – personalized for a specific user. User trust protects and elevates the reputation of your brand. 

RAG-LLM Interaction via Data Products 

Data products are powering the RAG revolution. These reusable data assets combine data with everything you need to make them independently accessible by authorized users.  

A data product injects trusted, up-to-date internal data into a RAG framework in real time. The combination of reliable inputs and speed lets you integrate your customer 360 or product 360 data from all relevant data sources, and then turn that data and context into relevant prompts. These prompts are automatically fed into the LLM along with the user’s query, enabling the LLM to generate a more accurate and personalized response.

With a data product platform, data products can be accessed via API, CDC, messaging, or streaming – in any combination – allowing for data unification from multiple source systems. A data product approach can be applied to multiple RAG use cases – delivering insights derived from an organization’s internal information and data to: 

• Speed up issue resolution

• Design hyper-personalized marketing campaigns

• Generate personalized cross-/up-sell recommendations for call center agents

• Detect fraud by identifying suspicious activity in a user account 

Discover the K2view Data Product Platform.