What do you think about how the large language models (LLM) actually work? Whenever you ask your language model something, it must give you the correct information and not the wrong. How is this possible? It is possible because of Retrieval Augmented Generation (RAG).
While asking any query to the models, RAG plays an important role; it updates the database with updated information. So, when the user discusses a query with large language models. Then, the large language model sends that query to its database. Moreover, the database searches for a similar solution to our query and returns it to the large language models.
What is Retrieval Augmented Generation?
Retrieval augmented generation (RAG) is a technique that provides more accurate information to the database to enhance large language model data. It combines the information from retrieval systems and neural language models to give improved results. Sometimes, the database contains outdated or faulty information, so RAG plays an important role in removing them.
Furthermore, the LLMs work on words; they do not know the meaning of the user’s query. But, RAG sets up the data in such a way that it connects it with words. Using RAG in large language models has benefits, such as they provide the latest information. Users can also access the model’s sources of gathering information.
Benefits of Retrieval Augmented Generation
RAG has major benefits for users as well as the models. Here, we will learn some of the benefits of the techniques. So, let’s check them out:
1. Provides more accuracy
Implementing RAG in the models can make the AI deliver the answer more accurate, relevant, and up-to-date. It allows developers to add and update the latest information to their language models. It is also useful for directly connecting the model to social media and websites from the system.
2. Improves user trust
We always want a platform that provides answers that align with our demands. The RAG is similar to this. It provides the latest and most relevant information that users require. So, regularly updating the information and providing it to users is beneficial. Furthermore, accessing the information’s source gains the user’s trust.
3. Time and cost-effective
LLMs provide the information in seconds, but the training period and cost of the training are high. However, RAG is time- and cost-effective; it provides accurate data for the LLM’s dataset. It makes AI more useful and accessible to users than LLMs.
How does RAG work?
Retrieval augmented generation is one of the major parts of large language models. If RAG is not connected to the LLM, it continues to provide the information and data it is trained on. However, after the implementation of RAG, when users ask any query to the LLM they get accurate information. The LLM breaks the query into pieces and gets the information from external sources such as web pages, records, etc.
RAG works as an external news provider to LLMs. The models are trained in a way that contains the information based on their training. However, if the data is added in LLM after training, it will be called external data. These data include documents, APIs, and more. In LLM, the data is converted into a numeric form known as a vector database, which the model understands.
Moreover, the next step is to check the relevance of the data. The user’s query will also reach the LLM in vector form. It checks the vector database to see if it has a solution for user queries. After it finds the relevant information due to Retrieval augmented generation updating LLMs regularly, it provides it to the users through vector representation.
How does the user ask their query with LLM when it is a language model? It is through prompt engineering. It makes the query understandable for the LLM; similarly, the model generates the answer. Updating the external data is important because the information may become outdated.
Challenges of RAG
Every useful thing must have some challenges. Similarly, using RAG also causes many challenges, which you must remember before using it.
1. Accessing third-party data integration from LLM may require third-party sources. However, to build and maintain the integration, it require copying the information and adding it to the LLM. It also requires technical resources. So, data maintenance can take a lot of work for engineers to manage.
2. RAG can cause the information to delay response. It depends on the data source size, network or connectivity problem, data sources and query quantity needed for access, and more. These factors make managing Retrieval augmented generation difficult.
3. Accessing third-party data integration can steal sensitive data from the model. So, precautions must be taken while handling the integration of the data. If the precautions are not taken on time, it can cause GDPR or HIPAA, which means violation of laws.
Last Line
Without Retrieval augmented generation (RAG) using LLMs is not beneficial. It is because the LLM is based on its training data, however, updating the data is important. So, RAG keeps updating it from external data such as web pages, documents, and APIs. Although it has many challenges, remembering them is necessary.
Also Read About: OpenAI o1: The AI Brain That Thinks Twice!