What is Retrieval-Augmented Generation?
Retrieval-augmented generation (RAG) is an artificial intelligence system that can generate more accurate responses than a single LLM model. Because It uses two or more LLM models: retrieval and generator.
The retrieval model can find and pull information from files or databases. The generator model uses findings to create more accurate, current, and useful answers. As a result, RAG increases LLM models' capabilities by allowing them to use data outside of their training data
In this article, we will explore what RAG is, its use cases for businesses, what issues it has and how to overcome these.
How does Retrieval-Augmented Generation work?
The main idea behind Retrieval-Augmented Generation is that the LLM models alone can only generate information from the data they were trained. However, the RAG system introduces another layer of retrieving information from external or internal data sources. The user query and the relevant information are used to generate a more accurate response. Here is how the process works:
User query
The user types the question using a prompt to the system, which starts the process.
Embedding creation
Transforming data and user responses into numerical representations (vectors) and embeds data in a three-dimensional space, also known vector database, so LLMs can make sense of it. In this space, information with closer relationships is placed near each other and vice versa.
Relevant information retrieval
Receiving relevant information from files or databases. For example, RAG can look through PDF files and pick out the things that match the terms from the user's query.
Re-ranking (Optional)
Comparing files with user queries to come up with a relevancy score, which can be used to find more relevant documents.
Response generation
Generating the response using a large language model like GPT 4, Claude, or the open-source models. Since the data is now structured in vector databases, LLMs can understand its contextual meaning and generate more accurate answers.
What Are the Benefits of Retrieval Augmented Generation?
1. Reduce hallucinations
You probably have used LLMs and found it to come up with answers. RAG mitigates this by matching responses to actual information in external or internal data sources. Because of that, it’s much more accurate than guessing or making things up (hallucinating), which LLMs are known for.
2. Use data without training LLMs
LLMs are limited to their training data, but with RAG, you can use any data without re-training the model. Need the system to specialize in a specific domain? No problem. Just change the reference documents retrieval can access. This makes it possible to create expert systems for domain-specific tasks quickly.
3. Access real-time information
Lastly, you can connect the retriever to access real-time data. For example, if you have information that changes daily and you need to include that in your answer, the retriever can do that. It can find information directly and give up-to-date responses.
What Are RAG Use Cases
RAG works for many use cases and domains. For example,
- Customer support - It can look up information about past FAGs and give accurate answers to customers.
- Research - it can summarize the latest papers
- E-commerce - it offers better product recommendations or search capabilities
- Healthcare - it can pull up medical guidelines and much more.
Essentially, RAG is effective for any industry where you want to build question-and-answer or search tools.
How To Make RAG More Efficient?
There is this new idea called "GraphRAG." Instead of storing data in vector databases like basic RAG, it stores information using graph-based knowledge representation, often knowledge graphs or semantic networks. As a result, it builds better relationships between data to make the information retrieval process more accurate.
That said, GraphRAG is not good for all use cases as well. Its issue is that it takes more time to process data and generate results. For example, it can perform better with complicated financial data, which need more accuracy but can take longer to process. However, it may perform worse in search tools that need faster process time, where traditional RAG excels.
Conclusion
Retrieval-augmented generation in AI is a processes where models can find information from your data sources (files and databases) and match with user queries to generate better answers. This way, you don't have to train the model with your data, which is a very expensive process and can range up to millions but have the same capabilities as if you have done so. As a result, businesses can use RAG tools to respond to end users with higher relevancy.
In this article, we also introduced a new idea called GraphRAG that makes retrieving parts even more efficient but tends to be slower in response generator. As a result, we have built a platform where you can choose a use case, that immediately chooses the right underlying system for you. That means you can add RAG functionality to your products or build new experiences quicker than ever before.
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