Artificial intelligence transforms ways of gathering information, responding to queries and automating daily tasks in businesses. Sometimes, though, AI models generate outdated, incomplete or inaccurate information. The solution is retrieval-augmented generation – a practice that combines information retrieval and AI-generated answers.

With the help of RAG, AI systems can retrieve relevant information from external sources before answering. As a result, AI applications become more helpful for businesses which require a response based on a particular document or database.

What Is Retrieval-Augmented Generation?

The RAG definition is quite straightforward – retrieval-augmented generation is an AI technique that allows retrieving relevant information from external knowledge sources and providing it to a language model as a context for generating a response.

So, if you ask yourself, “What is a RAG in AI?”, imagine an assistant who reviews the necessary reference materials before giving an answer. In contrast to an AI system that responds based only on the information gathered during its training, the AI generates an answer using relevant documents or up-to-date data.

A RAG description consists of three key elements: information retrieval, addition of context and answer generation.

How Does the RAG Model Function?

In most cases, the RAG model includes a retrieval mechanism along with a large language model (LLM). While the retrieval mechanism identifies the relevant information, the language model then uses that information to generate an appropriate response.

Thus, an employee who needs to know about the leave policy of the organization will get an answer based on the latest HR policy of the organization, and not just any generic answer.

The typical steps involved in this process are as follows:

Retrieval: The retrieval mechanism looks up the knowledge base for information related to the question posed by the user.

Augmentation: The retrieved information is then used to augment the prompt.

Generation: The language model takes into account both the question and the retrieved information to generate the answer.

Understanding the RAG Pipeline

A RAG pipeline is the process by which the documents, the retrieval method, and the AI model are connected.

Initially, the source documents are gathered and preprocessed. In case of very large documents, the same could be split into smaller parts known as chunks. The chunks could be converted into embeddings and stored in the vector database.

If a user queries a question, the query is searched for relevant answers via semantic search, keyword search, or both. The retrieved documents are further processed using the language model to produce an answer.

The performance of the pipeline relies on good source data, good retrieval, proper context, and evaluation.

Conclusion

A combination of information retrieval and AI-based generation is used to make AI apps contextually aware and practical. With the ability to first retrieve the information and then provide an answer, RAG can be useful for companies in developing assistants using their own information and documents. With correct information, an effective pipeline design, and evaluation, companies can develop better AI-based information solutions.

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