What Are Retrieval Strategies?

In the previous lesson, we explored indexing by comparing it to organizing a large library. Indexing places every piece of information exactly where it should be, making it easy to find later. We also briefly mentioned retrieval, which involves finding the exact information needed from this organized structure. Retrieval is about locating the specific book that perfectly answers our question, rather than just grabbing any book off the shelf.

Think of the retrieval process as asking a smart librarian for a book. When you ask a question, it's like telling the librarian what you're looking for. Instead of just pointing you to a random section, the librarian uses a sophisticated map (the index) that shows how all the books are connected based on their content. The librarian finds the exact spot on the map that matches your question. Then, they look around that spot for the books that are closest in meaning and relevance to your question. This way, instead of just giving you any book, the librarian provides you with the book that contains the exact information you're looking for.

This method of finding information is key to making RAG systems work well. It ensures that when we generate answers, they're not just correct, but also super relevant and helpful based on the context of what was asked.

What retrieval methods are used in RAG?

RAG employs several retrieval methods to integrate external knowledge into the generative process. These strategies are crucial for how RAG systems access and utilize relevant information from a knowledge base or dataset to enhance generation quality. Here are the primary retrieval strategies used in RAG:

• Sparse retrieval: Sparse retrieval techniques, like TF-IDF and BM25, rely on keyword matchingKeyword matching is a sparse retrieval technique where documents are retrieved based on the presence of specific keywords or terms.. These methods are fast and efficient but might miss subtle semantic relationships between words. Sparse vectors are representations where most elements are zero, typically used to encode the presence or frequency of specific terms.