How many times have you searched for a file you created but couldn’t remember where you saved it or what you named it? You might remember what it looked like or the content it contained, but locating it can become a time-consuming task.
Imagine being able to describe what you’re looking for in your own words—or better yet, simply telling an application what you want it to create, and it determines which relevant data to use.
Good news: this is no longer a vision for the future—it’s a reality today.
This post explores how Retrieval Augmented Generation (RAG) empowers Large Language Models (LLMs) with your enterprise data, enabling organizations to harness their information assets more efficiently.
The Building Blocks of an AI Powered by Enterprise Data
To integrate your enterprise data with an LLM, several key components are necessary. Understanding these elements is crucial to unlocking the full potential of AI within your organization.
Large Language Models (LLMs)
LLMs are advanced AI models trained on vast datasets to understand and generate human-like text. They serve as the foundation for interpreting questions and instructions, providing meaningful responses. Examples include OpenAI’s GPT-4o and Meta’s Llama 3.1. While these models are powerful, they are trained on general data and are not familiar with your specific enterprise information.
Everyone has now tried ChatGPT and gotten a taste of what’s possible. However, using it can be inefficient because you have to constantly copy chunks of text or images into your chat to get it to do something. Behind the scenes of ChatGPT is an LLM like GPT-4o. There are also many open-source versions that are quite powerful and can be hosted in your own controlled environment, such as Meta’s Llama 3.1. However, these models weren’t trained on your data.
The Challenge with Enterprise Data
Your organization possesses a wealth of data that LLMs don’t inherently understand. Integrating this data with LLMs presents challenges:
Manual Data Input: Copying and pasting data into prompts is inefficient and impractical for continuous use.
Fine-Tuning Limitations: Fine-tuning an LLM with your enterprise data can be complex and doesn’t account for new data as it gets created and updated in real time.
Security: Not all data is accessible to everyone within the organization, so care must be taken to limit which data can be accessed by the LLMs
Introducing Retrieval Augmented Generation (RAG)
RAG offers a solution by allowing LLMs to access and utilize your enterprise data without manually finding and clipping the needed parts. This technique significantly boosts productivity within organizations by automating the retrieval of relevant information, ensuring that the AI’s responses are informed by your organization’s specific knowledge base.
Even though we often discuss RAG in the context of chat use cases, it’s extremely powerful for creating agentic workflows, where agents have access to your enterprise data and can make decisions semi-autonomously.
Understanding the Retrieval Process
To appreciate how RAG works, it’s essential to grasp some underlying concepts:
Context Window
A context window refers to the amount of information an LLM can process in a single query. While some modern models support up to 128,000 tokens (roughly equivalent to words), practical limitations often reduce this number. You might have tried using ChatGPT and received an error like, “The message you submitted was too long, please reload the conversation and submit something shorter.” Large prompts can result in errors or inaccurate responses. Therefore, it’s vital to select and provide only the most relevant data to the LLM.
Chunking
Chunking involves breaking down large documents into smaller, manageable pieces, or “chunks.” This process serves several purposes:
Fits Within Context Windows: Smaller chunks ensure the data stays within the LLM’s processing limits.
Enhances Focus: It directs the LLM’s attention to specific, relevant information, improving response accuracy.
Facilitates Efficient Search: Chunking allows for more precise indexing and retrieval of data based on content and meaning.
There are numerous chunking strategies that you can use, but a simple way to think of a chunk is as a paragraph of a document. Each of these paragraphs will get a corresponding vector embedding (a multi-dimensional numerical representation) that captures the meaning, effectively capturing the concepts and major keywords mentioned. Integrating knowledge graphs further enhances this process by identifying relationships between entities, ensuring that connected and relevant information is retrieved for even more precise results.
These techniques enable you to type in something like “summarize all documents written by our founders that discusses the founding principles of VAST,” and it finds documents that match exactly that, even though the documents may not contain those exact words.
Security
Enterprise data often comes with strict access controls, and the introduction of RAG must adhere to these security protocols. When a user asks a question, the system should only retrieve relevant documents that the user has permission to access. This requires preserving access controls across all the data sources integrated into the retrieval system, ensuring that security is maintained at every step. By respecting these permissions, RAG can operate within your organization’s security framework, protecting sensitive information while still providing powerful AI-driven insights.
Retrieval Augmented Generation
Retrieval augmentation automates the process of finding and attaching relevant data chunks to user queries. Here’s how it works:
Query Embedding: The user’s question is converted into a vector embedding.
Search for Similar Embeddings: The system searches for vector embeddings and/or knowledge graph relationships that closely match the query, corresponding to relevant data chunks.
Retrieve Relevant Chunks: It gathers the most pertinent pieces of information from your enterprise data.
Construct the Prompt: A new prompt is created, combining the retrieved data chunks with the original question and additional instructions, ensuring it fits within the context window.
Generate the Response: The LLM processes the prompt and returns an informed answer to the user.
This method eliminates the need for manual data handling, allowing users to simply ask their questions and receive accurate, context-rich responses. It’s essentially automating the process of generating a prompt along with automatically copying and pasting all the relevant parts of the documents needed, without you having to search for the right documents yourself—far more efficient!
Logging
Implementing logging in RAG applications is crucial for several reasons:
Auditing: Keeps a record of queries and responses for compliance and review.
Performance Improvement: Helps refine prompts and retrieval strategies.
Caching: Stores frequent responses to reduce processing time and computational costs.
Model Fine-Tuning: Provides data that can be used to further train and improve the LLM.
Monitoring: Tracks the system’s effectiveness and identifies areas for enhancement.
Even if it’s not a regulatory requirement, there are numerous benefits to logging prompts, chunks used, and responses. This log data can be streamed to systems like Kafka and ultimately stored in a structured data format where it can be easily queried later. By streaming log data into structured formats, organizations can analyze and optimize their AI systems over time.
VAST Data: Supporting Your AI Journey
VAST Data offers a unified data platform capable of storing exabytes of files, objects, and structured data—all accessible to every stage of the AI data pipeline without the need to move data across systems.
With VAST, your RAG applications can:
Store and Retrieve Chunks: Integrates directly with popular query engines or an embedded Spark cluster.
Simplify Security: VAST’s single security namespace ensures consistent access controls across all your structured and unstructured data, simplifying the security process.
Scale with Your Needs: Handle data scaling up to exabytes and trillions of data rows, ensuring your AI infrastructure grows with your organization.
Provide Direct Access: Link users to the exact files or objects used in generating responses, enhancing transparency and usability through standard file or object protocols.
Optimize Continuously: Store logs and data for ongoing improvements and fine-tuning.
By offering these building blocks, VAST Data enables organizations to embrace AI technologies at any stage of their journey.
Embrace the Future of Enterprise Data Access
Retrieval Augmented Generation represents a significant advancement in how organizations interact with their data. By leveraging RAG, you can unlock the full potential of your enterprise information, making it more accessible, actionable, and valuable.
VAST Data provides all the building blocks necessary to embrace AI, no matter where you are in your journey. On October 1st, be sure to attend our Cosmos event to hear how VAST is making Enterprise AI simple, secure, and truly real-time by dramatically simplifying AI data pipelines for RAG operations.
