As an example, say that you have a product catalog with files on each product. Some of those products may be very similar, with minor differences in terms of size or additional functionality depending on which version you look at. When a customer asks about a product, you would want your LLM to respond with the right information around the uk whatsapp number data category and around any specific product features too. You would not want your LLM to recommend one product that doesn’t have the right features when another in the same line does. Product documentation may also reference other information, e.g., by having a link in the document which means the chunk returned may not offer the end user the full picture.
To overcome the potential problem around including the right level of detail, we can combine RAG with knowledge graphs, so that we can point to more specific files with the right data for a response. A knowledge graph represents distinct entities as nodes within the graph and then edges indicate relationships between the specific entities. For instance, a knowledge graph can provide connections between nodes to represent conditions and facts that might otherwise be confusing to the LLM because they might otherwise seem similar.