RAG (Retrieval-Augmented Generation) in GenAI:
What It Is and Why It Matters
Current Technology and What is RAG?
Generative AI is a technology that everyone is using, but what makes Gen AI this popular?
RAG (Retrieval-Augmented Generation) is a technology which is used by Gen AI to build its answers so accurate and qualitative. So how does RAG work? As its name suggests it retrieves information from vast knowledgebase like books, websites or databases, and on basis of this knowledge the information is generated.
How RAG works?
RAG basically works in said manner as Retrieve the information and generate response, but this information Retrieval process is done on vast amount of knowledgebase and the information is retrieved using techniques like semantic analysis, keywords matching and vector search. The generation task relies more onto NLP Techniques to formulate the words in Human Readable understandable Language.
Why RAG Matters?
Retrieval Augmented Generation matters because of its reliability. RAG provides answers on basis of information and it is not creating any in conceptual information by hallucinating. Also, RAG has increased the accuracy in GEN AI sector. RAG is being used in Text Generation, Summarization, creative writing etc. RAG is a perfect example for text generation in Specific Styles. The text generation in William Shakespeare’s English is an example of generating text using RAG. Here the model retrieves information on Shakespeare’s content analyse it and generate creative style text.
RAG is latest technology but it also has some drawbacks like if the information retrieved from the knowledgebase is incorrect or the information is wrong in the database itself the response generated will be incorrect. Also due to information retrieval it is most important that privacy is maintained about information. Due to RAG confidentiality and security comes to risk.
Benefits of RAG
RAG has become popular due to its accuracy in answers and has significantly reduced the chances of hallucination. RAG guarantees proof for answer or response it has generated information on. It has increased efficiency and has reduced time and made Gen AI more reliable. Rag currently is used in many real-world applications like Customer Service Chatbot or any content creation services. RAG as compared to traditional text generation mechanism is more powerful. It has subject or domain matter expertise and can give excellent result. RAG has provided a better hand by allowing real time updates in knowledgebase.
Conclusion
In essence, RAG provides GEN AI capabilities to integrate retrieval
mechanism. It overcomes
many traditional approaches used for
text generation. RAG provides
accurate information on learning from underlying knowledgebase. It is useful for applications relying on real time information, and scaling and handling
of huge amount of data. It has wide scope
of applications and provides accurate and relevant responses.
