Retrieving external information for your RAG
Understanding how RAG leverages external knowledge is crucial for appreciating its ability to generate factually accurate and informative responses. This section discusses various retrieval techniques, strategies for integrating retrieved information, and practical examples to illustrate these concepts.
Understanding retrieval techniques and strategies
The success of a RAG model hinges on its ability to retrieve relevant information from a vast external knowledge base using one of the commonly used retrieval techniques. These retrieval methods are essential for sourcing relevant information from large datasets. Common techniques include traditional methods such as BM25 and modern neural approaches such as DPR. Broadly speaking, these techniques can be classified into three categories: vector similarity search, keyword matching, and passage retrieval. We will discuss each of them in the following subsections.
