#RAGMatters : Why Retrieval-Augmented Generation is Revolutionizing AI
AI on Steroids? Explained!
We've all likely used ChatGPT at some point in our lives. These large language models (LLMs) are impressive, allowing us to ask questions like "Explain the concept of a black hole" , “What is Love?”, “How to get Abs in 10 minutes?”.
If you are using ChatGPT ( LLM ) for your day to day activities, you might as well make it work for you.
This is where Retrieval-Augmented Generation (RAG) comes in. It's a framework that combines the Generative AI (GenAI) with retrieval-based methods to create a more powerful and contextually aware system.
These days GenAI is being used by almost every other company such as Veritas, Cohesity, Druva, Rubrik, Commvault, Google, Amazon, Microsoft, etc.
Understanding the Building Blocks:
Retrieval-Based Systems: In traditional retrieval based systems, you typically have two main components, a large database of text and an input query. You will feed in the input query and the responses are generated by selecting text from the large database.
GenAI: It is a branch of AI that focuses on creating new content. This content can belong to any format - images, videos, text, audio. It is done by using models that are trained to generate outputs that seem as if they're mimicking humans.
A Three-Step Process
User Query: You ask a natural language question relevant to your specific needs.
Targeted Information Retrieval: The RAG system performs semantic search into your private database, identifying the most relevant information (articles, reports, etc.) that directly address your query.
Enhanced Response Generation: The retrieved information is fed alongside your query into the LLM. This allows the LLM to generate a response that is not only factual and relevant to your specific context but also leverages the insights from your own data.
Why RAG Matters:
Combats LLM Hallucination: LLMs trained on general datasets might struggle with questions outside their domain. RAG prevents this by ensuring the LLM focuses on information directly related to your needs, minimizing the risk of irrelevant or inaccurate responses.
LLMs Don't Know Everything (Yet): LLMs like ChatGPT aren't trained on your enterprise data. So, questions specific to your company's operations or internal knowledge base wouldn't be accessible to them.
Focus and Accuracy: Imagine having your own unpaid professional employee working for you on data sources you provide. RAG allows LLMs to act in a similar way, providing accurate responses based on your specific context.