Earlier quoted context omitted.
Amazon Rufus has been mildly successful for me. I think the failures I've experienced with it are mostly because the product I'm looking for doesn't exist in the catalog.
It's been an absolute fucking disaster for me. It hallucinates endlessly and its searches are terrible. It even managed to confidently gaslight me about there being a VAT invoice available for a specific product. I noticed yesterday when browsing on mobile that there used to be a box where I could search reviews and it got swapped with a Rufus box. I guess somebody needs to juice their engagement numbers for an inves…
Don't classify, hallucinate
31–40 of 118 posts
Re: Don't classify, hallucinate
#32Can anyone explain why LLMs are so bad at finding products (their webpages) with given specifications? You'd think they would have solved it by now.
Asking an LLM for a list of every county in the USA for example, or every county with a population of more than 100,000 people.
Even if those county names and their populations are mixed up in their weights, the nature of next-token-prediction does not lend them to effectively answering comprehensive, detailed questions like that.
An agent system build on top of an LLM can do it, if it has access to tools which can help access eg a table of counties and then filter them with SQL or Pandas or similar.
Re: Don't classify, hallucinate
#33Just this week I tried doing something similar with a nasty vibe-coded codebase I was trying to organize. I had Gemini Flash 3.6 classify each function/method in a similar way, giving a few plausible classifications for each (one agent per method). It didn't end up being very useful - I ran a comparison where I just had a bigger agent do the organization in a more straightforward way, and that had better results. I d…
Re: Don't classify, hallucinate
#34Re: Don't classify, hallucinate
#35Re: Don't classify, hallucinate
#36Can anyone explain why LLMs are so bad at finding products (their webpages) with given specifications? You'd think they would have solved it by now.
LLMs aren't architected to handle filter-style comprehensive search without setting them up with additional tools. Asking an LLM for a list of every county in the USA for example, or every county with a population of more than 100,000 people. Even if those county names and their populations are mixed up in their weights, the nature of next-token-prediction does not lend them to effectively answering comprehensive, de…
Considering that agents are not a new concept, why isn't this a solved problem by now?
Re: Don't classify, hallucinate
#37Re: Don't classify, hallucinate
#38Re: Don't classify, hallucinate
#39This is basically HyDE (Hypothetical Document Embeddings), no? I had tried this approach in the past, worked with limited success.
Re: Don't classify, hallucinate
#40Earlier quoted context omitted.
LLMs aren't architected to handle filter-style comprehensive search without setting them up with additional tools. Asking an LLM for a list of every county in the USA for example, or every county with a population of more than 100,000 people. Even if those county names and their populations are mixed up in their weights, the nature of next-token-prediction does not lend them to effectively answering comprehensive, de…
Yes, I was assuming they'd use external tools. Using only the raw LLM doesn't sound like a good strategy. Considering that agents are not a new concept, why isn't this a solved problem by now?
We've got the early LLM-based AI agents in 2023, and it only became a popular, mainstream thing in 2025 - with Claude Code.