Retrieval-Augmented Generation (RAG) is the method of refining the output of a substantial language model by referring to a credible knowledge base beyond its training data sources prior to generating a response.
In this article, we will explain how it actually works in simple words.
Imagine we have a user. The user could be a person, a bot, or another application, all seeking answers to specific questions. For example, they might ask, “What was the churn rate in Q1 for customers in the south region?”
The initial part of this question is typically within the scope of a general Large Language Model (LLM). However, the specificity of data required for “Q1 from customers in the south” is not directly available in LLMs, as it is unique to the business and changes over time.
To manage this, multiple data sources might be necessary. These could include PDFs, other business applications, or even images. Accessing the right data is crucial to answering such specific queries accurately.
The Role of Vector Databases in RAG
Once you gather the necessary data, it is entered into a vector database. This database organizes both structured and unstructured data into a mathematical array format, which is more comprehensible for machine learning and generative AI models compared to raw data. By querying this vector database, you retrieve an embedding that contains data relevant to your question.
This embedding is then reintroduced to the LLM, enriching the original prompt with precise, sourced data. The LLM processes this enhanced prompt and delivers the answer to the original question, ensuring accuracy and relevance.
As new data enters the vector database, it updates the embeddings relevant to ongoing queries such as the churn rate in Q1. This continuous updating ensures that subsequent queries receive the most current and relevant information.
Mitigating Risks in AI-driven Data Analysis
The quality of data entering the vector database is very important for the outputs produced. Ensuring clean, well-governed, and properly managed data is essential. Additionally, the transparency of the LLMs used in the process is also crucial. It’s important to use LLMs that are transparent in their training processes to ensure reliability and accuracy.
At NextBrain AI, we use the latest AI technology to deliver precise data analysis and actionable business insights, without the complexities often associated with technical implementations. Schedule your demo today to experience firsthand how our solution operates.
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