With agentic commerce protocol / unified commerce protocol open ai and gemini are trying to solve this problem.
replyThe idea is to make structured queries using these protocols which can be used to fetch top products matching the user needs instead of just relying on semantic search.
https://developers.openai.com/commerce/specs/file-upload/pro...
It gets worse: shopping agents are hostile adversaries to Amazon unless they're paying Amazon and they've agreed to be friendly agents. No agent that won't betray you to an Amazon pricing strategy is going to be allowed access to Amazon structured data. They might even be fed poisoned data to discredit them.
replyBut you'll be amazed by the abundance.
An LLM can read websites, right? And turn them into structured data.
replyIt can do that on run time, but it does not store data like that. The data is typically stored as embeddings in which it is hard to query data in a structured form. Example give me all products whose price is less than 200$ vs suggest me products for my spouse's birthday.
replyThen they shouldn't store the data as embeddings.
replyInstead: use an LLM to build a large (old-school) database of products with all their specifications. The LLM can also build the schema for that database as it finds more data.
Then use an LLM to query that database based on the user's specifications (+ add some intelligence to find nice suggestions for a birthday if wanted, but I'd consider that an extra).