Using the Pinecone vector database in .NET [Video]

Build RAG-powered LLM applications using the tools you know with a managed vector index in Azure.

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If you’re building generative AI applications, you need to control the data used to generate answers to user queries. Simply dropping ChatGPT into your platform isn’t going to work, especially if you’re using proprietary data that wasn’t part of the initial training set for the large language model (LLM) you’re using.

Without some form of grounding, your AI is liable to quickly and randomly generate plausible-sounding text as it tries to predict the output associated with a user’s prompt. It’s not surprising that the LLM “hallucinates” like this, as it’s purely a way of statistically generating text at a syllable level. Instead of working with words, it’s using its neural network to navigate through the most likely path in a multidimensional semantic space.

Reducing AI risk with RAG

There’s more than one way to reduce this risk: You can …