Understanding Retrieval Augmented Generation: Part I
A Little Introduction
In the world of AI, models like GPT-4 and Gemini have been getting the spotlight — and for good reason. These transformative models are capable of generating text, answering questions, and assisting with a wide range of tasks based on the vast amounts of data they were trained on. However, while these capabilities are impressive in casual interactions, they don’t always deliver the specific, actionable insights that enterprises need.
The main reason for this limitation is that these models are limited to the knowledge they were trained on, which often doesn’t align with the unique needs of a business. They don’t know anything about your company, products, or services, and therefore, they can’t answer the questions your customers (or your company) care about. This is where Retrieval Augmented Generation (RAG) comes into play.
To fully explore the potential of RAG, we’re launching a series of blog posts that will cover different aspects of this technology. We’ll start with a foundational understanding and delve deeper into the technical details in the second part.
What is Retrieval Augmented Generation (RAG)?
RAG combines the generative power of LLMs with a retrieval mechanism that pulls relevant information from an external knowledge base. In practice, this means that before the LLM generates an answer, it retrieves information related to the question from an external source, allowing it to produce more accurate and specific responses.
To understand this better, imagine you’re asked about the features and price of a specific product your company sells. Instead of simply asking the same question to ChatGPT, you first look up the product specification sheet. Then, you copy and paste the relevant text into your ChatGPT chat before appending your question, like this:
Based on the following information:
<Product X specification sheet>
What are the features and price of X?This gives the LLM the context it needs to generate an answer that is both coherent and accurate, directly based on your product.
Use Cases in Enterprises
Let’s delve deeper into some enterprise use cases in which RAG can be particularly beneficial:
Product/Service Inquiries
- Challenge: Customers often inquire about specific features of products or services that your company offers, which is not covered by any LLM’s training data.
- Solution: By retrieving the most recent product or service information from an internal database, RAG ensures that the customer receives an accurate and detailed answer. This approach also reduces the likelihood of LLM hallucinations — a phenomenon where the model generates incorrect or fabricated information.
Regulatory Compliance Advisor
- Challenge: A financial institution must comply with constantly changing regulations. An LLM trained a year ago may not be aware of recent changes in compliance laws, potentially leading to outdated or inaccurate advice.
- Solution: RAG can dynamically retrieve the latest regulatory updates from a legal database or compliance management system, allowing the model to provide legal or regulatory advice that adheres to current laws. This reduces the risk of costly compliance issues, such as fines or legal penalties.
Benefits of RAG over Standalone LLMs
Access to New Information
A standalone LLM is limited by the information it was trained on, which may become outdated over time. RAG overcomes this by retrieving the latest information from a knowledge base.
Contextual Accuracy
By combining LLMs capabilities with the retrieved information, ensures that the generated answer is contextually relevant. Adding relevant information to your prompt, also reduces LLM hallucination.
Cost Efficiency
Standalone LLM may reach similar accuracy than RAG by training it using up-to-date data from your company, but the main problem here is the cost of training and maintaining updated would require multiple trains over-time.
Enhanced Customer Experience
For customer-facing roles, RAG help provide instant, accurate responses. Whether it’s troubleshooting, product details or order tracking, a chatbot equipped with RAG can access the relevant information, reducing response times and improving accuracy.
Conclusion
Retrieval-Augmented Generation (RAG) represents a significant advancement over traditional standalone LLMs. By enabling models to access and use the most current and relevant information, RAG solves many of the challenges enterprises face, such as dealing with outdated data, ensuring compliance, and improving customer service. As enterprises continue to seek ways to leverage AI for better decision-making and efficiency, RAG is poised to become an essential tool in the AI toolkit.
As we move forward in this series, we’ll dive deeper into the technical mechanics of RAG, including how vectors and embeddings work, how to build a knowledge base, and how to implement a RAG system within an enterprise. Stay tuned for our next post, where we’ll unpack the technical details that make RAG such a powerful tool in the AI toolkit.
