Category: RAG
-
In real-world applications, semantic similarity search alone is not always sufficient to meet information retrieval requirements. Beyond semantic relevance, results often need to satisfy specific constraints, such as belonging to a particular category or excluding certain values. When performing similarity search across the entire dataset, the retrieved results may be semantically relevant to the query…
-
Traditional RAG, which I covered in my previous post, uses dense vector search to retrieve documents that are semantically similar to the user’s query. This approach is especially useful for open-ended or natural language questions, where users describe their intent without necessarily knowing or using the exact terms found in the documents. However, relying solely…
-
Although an LLM is trained on a vast amount of data and is capable of generating high-quality text, it has inherent limitations, such as being restricted to the knowledge available up to its training cutoff and lacking access to domain-specific or private knowledge bases. Retrieval-Augmented Generation (RAG) is widely used to overcome these limitations by…
