From the course: Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q

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RAG in SQL query context

RAG in SQL query context

From the course: Text to SQL: Amazon Redshift Serverless for Generative SQL in Amazon Q

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RAG in SQL query context

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- [Instructor] As we explored earlier, we sent an SQL query to a LLM. The response may produce poor-quality results. However, if we throw more data into our prompt, we may increase our response rate. In natural language processing, to enhance our prompt, we may try the technique retrieval-augmented generation, or RAG. This is when we provide external data to the LLM to enhance the response message. When we bring in external data, we're providing more context to the LLM to enhance the results of the response via question and answering. In RAG, we may provide the LLM with context such as uploading PDF files, social media, websites, and other encoded data from PGVector databases. We know that using a prompt alone can sometimes produce inaccurate responses. To overcome this problem, we may walk through a simple illustration of a RAG process. In the retrieval stage, we can collect data from our organization's internal databases or knowledge base, such as learning management system or PDF…

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