- A vector database, which stores document embeddings, scales quickly and supports distributed storage for advanced indexing and vector querying.
- A vector library, which is a faster, lighter way to hold vector embeddings.
- Vector support integrated into the existing database to store vector embeddings and support querying.
The best choice depends on your specific circumstances. For example, a vector-native database is the most robust method, but it’s too expensive and resource-heavy to be practical for smaller organizations. A vector library is faster and best for times when latency is the enemy, while integrating vector capabilities is easiest but doesn’t scale well enough for heavy enterprise needs.
3. Build a solid retrieval process.
It’s right there in the name – RAG is all about retrieving the right data to build accurate responses. However, you can’t simply point your RAG infrastructure at data sources and expect it to retrieve the best answers. You need to teach RAG systems how to retrieve relevant information, with a strong emphasis on relevance. Too often, RAG systems over-collect data, resulting in excessive noise and confusion.
“Experimental research showed that retrieval quality matters significantly more than quantity, with RAG systems that retrieve fewer but more relevant documents outperforming in most cases those that try to retrieve as much context as possible, resulting in an overabundance of information, much of which might not be sufficiently relevant,” observes Iván Palomares Carrascosa, a deep learning and LLM project advisor.
