In the initial wave of AI adoption, the focus was on making unstructured data discoverable. We used techniques like chunking and vector search to find the relevant parts of documents to answer a question, then provided that as context to the model.
What I’ve seen since are more complex use cases: giving agents not only access to knowledge but also the capability to execute actions. A great example is the ability to run SQL queries, allowing LLMs to tap into structured data, not just unstructured content. While this is a popular use case in hackathons and demos, I’ve seen these kinds of projects often get stuck in PoC land, unlike standard RAG implementations. Getting most queries right is surprisingly simple, but Text-to-SQL solutions can be sneaky, and incorrect answers don’t always throw errors.
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