In-Depth Understanding of Vector Search for RAG and Generative AI Applications

Author(s): Talib

Originally published on Towards AI.

I will start by describing why we need a vector search for RAG, and how vectors and vector databases work, and then focus on the Azure AI search.

You might have used large language models like GPT-3.5, GPT-4o, or any of the other models, Mistral or Perplexity, and these large language models are awe-inspiring with what they can do and how much of a grasp they have of language.

So, today I was chatting with an LLM, and I wanted to know about my company’s policy if I work from India instead of the UK. You can see I got a really generic answer, and then it asked me to consult my company directly.

The second question I asked was, “Who won the last T20 Worldcup” and we all know that India won the ICC T20 2024 World Cup.

They’re large language models; they’re very good at next-word predictions; they’ve been trained on public knowledge up to a certain point; and they’re going to give us outdated information.

So, how can we incorporate domain knowledge into an LLM so that we can get it to answer those questions? There are three main ways that people will go about incorporating domain knowledge:

Prompt Engineering: In context learning, we can derive an LLM to solve by putting in a lot of effort using prompt engineering; however, it will never be able to answer if it… Read the full blog for free on Medium.

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Published via Towards AI