When developing generative AI applications, especially those utilizing proprietary data, it’s crucial to manage the data used to generate responses to user queries. Simply integrating a pre-trained large language model (LLM), such as ChatGPT, into your platform won’t suffice if your data wasn’t included in the model’s initial training set. Without a way to ground the AI’s responses in relevant data, it’s likely that the AI will generate plausible-sounding, yet random, text. This phenomenon occurs because the model is essentially predicting the most probable next word or phrase based on statistical patterns, which can lead to “hallucinations”—responses that sound logical but are disconnected from reality.
To mitigate this risk, there are two main strategies developers can consider: fine-tuning the LLM with proprietary data or utilizing retrieval-augmented generation (RAG). The RAG approach is particularly popular, as it combines search techniques with generative AI to produce more accurate and relevant results. Search engines like Bing, for instance, use RAG by running a search query, retrieving relevant data, and then generating a response based on the search results. This methodology is at the heart of many generative AI workflows, such as those powered by LangChain, LlamaIndex, Haystack, and Microsoft’s Semantic Kernel.
However, integrating a traditional database with an LLM presents a challenge: most databases, whether relational or NoSQL, lack a semantic model that would allow direct communication with the LLM. In other words, these databases don’t inherently understand the meanings or relationships behind the data they store. To make a database compatible with an LLM, you need to bridge this gap by adding a layer that enables semantic search—a process that converts data into a format that an LLM can interpret and use effectively.
This is where the concept of vector databases comes into play. A vector database stores data alongside a vector index, allowing for efficient searches based on semantic similarity rather than just exact matches. By using a vector index, you can create a system that enables quick and accurate comparative searches, which are essential for a RAG-based AI application. Microsoft’s integration of vector indexes into its search engine infrastructure is a prime example of how LLMs and search engines can work together. If you’re building a RAG-grounded AI application, you’ll need to create your own search engine tailored to your data by implementing a vector index that allows for semantic searches. Recent updates to Microsoft’s database properties and services reflect the growing importance of these tools in enabling sophisticated AI-driven applications.