Vector database growth can lead to latency. Up to 40% of any retrieval augmented generation (RAG) transaction is made by creating a vector from the original request and matching that against entries in the vector database? so any improvements to performance can have a significant impact.
Data set growth and an increase in transactions can significantly impact your overall performance. when testing hundreds or thousands of interactions? but scaling up to millions of transactions will be noticeable.
Vector data sets also grow as companies identify
More sources that can be used to improve the accuracy of responses? and as they expand their own data over time. For example? a product catalog with a thousand different stock-keeping unit (SKU) codes evolves and poland whatsapp number data changes over time. When customers ask a question about products? generative AI should reference the most up-to-date entries rather than older versions or versions that are no longer stocked. It is easier to update your vector database and use RAG to provide accurate data to your large language model (LLM) than to retrain your LLM every time there is an update.
In addition to RAG? there are newer techniques that can improve your responses to users.
These use the generative
AI system to improve prompts and responses in the background so that the user can benefit from the overall work carried out. One example of this is RAG fusion? where the AI system creates additional versions of the initial carry out risk assessments prompt provided by the user and then measures responses to those extra prompts alongside the original request. Using alb directory these responses? the user should get a more useful answer based on a sum of all the queries.
Similarly? Forward-Looking Active Retrieval (FLARE) is an example of a multi-query RAG technique that provides custom instructions in your prompt to the LLM. This encourages the LLM to provide additional questions about key phrases that would help the overall system generate a better answer for the user.