I can’t believe programming is now at the stage where advice like "first have the computer give you totally wrong answers, then just find a function that maps the wrong answers to the correct ones!" is a thing.

A common case I have is when you don't have classifications to begin with. For example, you need to find what users complain about most. I take embeddings of all records, then cluster the embeddings into semantic groups, then ask an LLM to take a random sample from each clustered group and create a classification for that group.

This method is sensitive to the thresholds (what is the maximum distance between embeddings for them to be still considered part of the same semantic group), so I run it all in an agentic loop where an agent tries different thresholds and clustering algorithms until it's satisfied with the result, plus it may deduplicate some groups.

I run it all on self-hosted hardware, so it costs nothing to leave it running for, like, a night, and as a bonus, none of the corporate data leaves the office. I think a rigid set of manually created classifications may not capture all the possible classifications that can exist. Needs a review by a human, though.

Nice trick. Couldn't you embed the query though, compare it to the embedding of the categories, then ship only categories that are close to it in the prompt to a smaller model?

> In the notebook, I compute a MiniLM embedding of every real Wayfair classification. I compute the embedding of the fake, hypothetical embedding from the LLM. I then dot product the fake embedding into the real ones to find the most similar. Producing: [the right answer]

Isn't this begging the question that the hallucinated classification will be more selective with respect to the real schema than the query itself? What would the dot product of <E(search query), E(schema)> have given?

Even if that is too vague, smaller LLMs are capable rerankers; return the top N matching true categories and ask for a contextual ordering.

I did something similar 10 years ago, but instead of llms I used word2vec to calculate a embeddings of product descriptions and map those to existing categories. The LLM approach is very likely better, but I'm curious what the cost difference is.

Prompt expansion of input to extra categories makes sense if your embedding isn’t working well. But on its own, why use the LLM at all? I think you could have demonstrated the original step first and then shown that it’s useful.

Did you validate this by running a A/B test? Main question is were you able to classify back into your known categories correctly all the time, or did the errors compound from the llm hallucination plus embedding search

This is basically HyDE (Hypothetical Document Embeddings), no? I had tried this approach in the past, worked with limited success.

I would propose the following, query vector store for 10 closest categories based on a query, feed it to an LLM, in the prompt ask it to produce a single digit 0-9 representing the number of the most appropriate choice. Use plain text prompt, dont inflate token count with JSON. There you go, you just drastically reduced the output pricing.

Additionally you could experiment with a reranker instead of an LLM or after reranking take top-3 results and then feed to LLM as input in order to reduce input token costs.

No? This is just giving up and hoping.

Smart trick, but assumes the “dumb” llm is smart enough not to derail into an article about the lives of South American red ants. Obvious exaggeration, the point being outcomes should stay strictly within topic, avoid unrelated bloat and hit the target.

Just 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 did find that Flash 3.6 High was >9x faster than Luna xhigh for this task, and got very similar results, though.

New embedding models support queries, so you don’t need to hallucinate a document before finding the nearest neighbor. Curious how it compares to this approach since you’d get to skip the LLM altogether.

Smart! I've done the same trick for resolving extracted intents to selection.

But if accuracy matters, you can't rely on embedding sort to get a closet match. With a real test set they usually don't hold up under scrutiny.

Everything in AI is like this. You get an idea, try it once or twice, "LGTM" and you ship. Then it never survives contact reality.

Embedding sort gives you a better shortlist than the whole list, but you will probably want a heavier model to vet candidates.

Interesting technique, but even if you're getting rid of hallucinations it seems there's still no guarantee of consistent classifications. If you need to do a semantic (embedding) search anyways, then how does this really help?

Had stumbled on this library in the past

https://github.com/aurelio-labs/semantic-router

I guess it is based on the same fundamentals as well.

I wonder how more accurate this is compared to just doing embedding similarity of the query vector and the category labels

Since you map each breadcrumb of the path, how do you deal with differing lengths that would be more appropriate?

I was in a project where we sent the whole taxonomy every request, 40k tokens + one article, ”plz classify”. This was before structured outputs. It was extremely expensive and still hallucinated. Good ol’ days.

Isn't search engines quicker than calling a LLM ? It might have a huge impact between a 20ms search engine call and a 2s LLM call for the end user.

Can 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.

I don't know the exact syntax any more, but I expect this could be solved by a single sql query that uses "inexact but close" queries and a bunch of indexes (and perhaps tags) on each category.

This is a really great trick, woah!

This is another riff on not embedding a full document, but doing a summarization of the document and embedding the summary for RAG. Nice usecase for high cardinality data!

Eh, maybe you should keep both paths. When LLMs eventually crawl the site to feed back to agentic shoppers, maybe they logically follow the more truncated less decorated path.

In the past, people would post advice on how to do something clever and useful yourself. Now, people post suggestions on how to talk out the side of their mouth to coax ther magic-8-ball slop generator to say something useful.