I was super-excited about vector search and embeddings in 2024 but my enthusiasm has faded somewhat in 2025 for a few reasons:
- LLMs with a grep or full-text search tool turn out to be great at fuzzy search already - they throw a bunch of OR conditions together and run further searches if they don't find what they want
- ChatGPT web search and Claude Code code search are my favorite AI-assisted search tools and neither bother with vectors
- Building and maintaining a large vector speech index is a pain. The vector are usually pretty big and you need to keep them in memory to get truly great performance. FTS and grep are way less hassle.
- Vector matches are weird. So you get back the top twenty results... those might be super relevant or they might be total garbage, it's on you to do a second pass to figure out if they're actually useful results or not.
I expected to spend much of 2025 building vector search engines, but ended up not finding them as valuable as I had thought.
The main problem isn’t embeddings, in my experience, it’s that “vector search” is the wrong conceptual framework to think about the problem
We need to think about query+content understanding before deciding a sub problem happens to be helped by embeddings. RAG naively looks like a question answering “passage retrieval” problem, when in reality it’s more structured retrieval than we first assume (and LLMs can learn how to use more structured approaches to explore data much better now than in 2022)
The problem with LLMs using full-text-search is they’re very slow compared to a vector search query. I will admit the results are impressive but often it’s because I kick off an agent query and step away for 5 minutes.
On the other hand, generating and regenerating embeddings for all your documents can be time consuming and costly, depending on how often you need to reindex
Not an apples to apples comparison. Vector search is only fast after you have built an index. The same is true for full text search. That too, will be blazing fast once you have built an index (like Google pre-transformer).
LLMs will always have the tool call overhead, which I find to be quite expensive (seconds) on most models. Directly using vector databases without the LLM interface gets you a lot of the semantic search ability without the multi-second latency, which is pretty nice for querying documents on a website. E.G. finding relevant pages on a documentation website, showing related pages, etc. Can be applied to GitHub Issues to deduplicate issues, or show existing issues that could match what the user is about to report. There are plenty of places where “cheap and fast” is better and an LLM interface just gets in the way. I think this is a lot of the unsqueezed juice in our industry.
The ultimate bottleneck in any search application is IOPS; how much data can you get off disk to compare within a tolerable time span.
Embeddings are huge compared to what you need with FTS, which generally has good locality, compresses extremely well, and permits sub-linear intersection algorithms and other tricks to make the most of your IOPS.
Regardless of vector size, you are unlikely to get more than one embedding per I/O operation with a vector approach. Even if you can fit more vectors into a block, there is no good way of arranging them to ensure efficient locality like you can with e.g. a postings list.
Thus off a 500K IOPS drive, given a 100ms execution window, your theoretical upper bound is 50K embeddings ranked, assuming actual ranking takes no time and no other disk operations are performed and you have only a single user.
Given you are more than likely comparing multiple embeddings per document, this carriage turns to a pumpkin pretty rapidly.
In my experience vector search (top 50 results) combined with reranking (top 5-15 of those 50 results) yields not only great results but is even quite performant if done right (which is not hard!).
Information about how Bing text search works appears to be pretty sparse though.
One of the great mysteries to me right now is how ChatGPT search actually works.
It was Bing when they first launched it, but OpenAI have been investing a ton into their own search infrastructure since then. I can't figure out how much of it is Bing these days vs their own home-rolled system.
What's confusing is how secretive OpenAI are about it! I would personally value it a whole lot more if I understood how it works.
So maybe it's way more vector-based than I believe.
I'd expect any modern search engine to have aspects of vectors somewhere - some kind of hybrid BM25 + vectors thing, or using vectors for re-ranking after retrieving likely matches via FTS. That's different from being pure vectors though.
Given that it's not documented also becomes a trust issue. OpenAI is clearly headed towards monetizing results and if search is biased / injected with unlabeled ads or questionable sources they become a new vector for both untrustworthy results and potential misdirection or misinformation.
You didn't build a search engine in 160 lines of code. You build a client for a search engine in 160 lines of code. The vector database is providing the search.
There’s a lot of previously intractable problems that are getting solved with these new embeddings models. I’ve been building a geocoder for the past few months and it’s been remarkable how close to google places I can get with just slightly enriched open street maps plus embedding vectors
That sounds really interesting. If you’re open to it, I’d be curious what the high-level architecture looks like (what gets embedded, how you rank results)?
You might be getting a good _recall_ rate, since vectorize search is ANN, but the _precision_ can be low, because reranker piece is missing. So I would slightly improve it by adding 10 more lines of code and introducing reranker after the search (slightly increasing topK). Query expansion in the beginning can be also added to improve recall.
What about re-ranking? In my limited experience, adding fast+cheap re-ranking with something like Cohere to the query results took an okay vector based search and made top 1-5 results much stronger
Reranking is definitely the way to go. We personally found common reranker models to be a little too opaque (can't explain to the user why this result was picked) and not quite steerable enough, so we just use another LLM for reranking.
Query expansion and re ranking can and often do coexist
Roughly, first there is the query analysis/manipulation phase where you might have NER, spell check, query expansion/relaxation etc
Then there is the selection phase, where you retrieve all items that are relevant. Sometimes people will bring in results from both text and vector based indices. Perhaps and additional layer to group results
Then finally you have the reranking layer using a cross encoder model which might even have some personalisation in the mix
Also, with vector search you might not need query expansion necessarily since semantic similarity does loose association. But every domain is unique and there’s only one way to find out
While embeddings are generally not required in the context of code, I am interested in how they perform in the legal and regulatory domain, where documents are substantially longer. Specifically, how do embeddings compare with approaches such as ripgrep in terms of effectiveness?
Models like bge are small and quantized versions will fit in browser or on a tiny machine. Not sure why everyone reaches for an API as their first choice
- LLMs with a grep or full-text search tool turn out to be great at fuzzy search already - they throw a bunch of OR conditions together and run further searches if they don't find what they want
- ChatGPT web search and Claude Code code search are my favorite AI-assisted search tools and neither bother with vectors
- Building and maintaining a large vector speech index is a pain. The vector are usually pretty big and you need to keep them in memory to get truly great performance. FTS and grep are way less hassle.
- Vector matches are weird. So you get back the top twenty results... those might be super relevant or they might be total garbage, it's on you to do a second pass to figure out if they're actually useful results or not.
I expected to spend much of 2025 building vector search engines, but ended up not finding them as valuable as I had thought.
We need to think about query+content understanding before deciding a sub problem happens to be helped by embeddings. RAG naively looks like a question answering “passage retrieval” problem, when in reality it’s more structured retrieval than we first assume (and LLMs can learn how to use more structured approaches to explore data much better now than in 2022)
https://softwaredoug.com/blog/2025/12/09/rag-users-want-affo...
On the other hand, generating and regenerating embeddings for all your documents can be time consuming and costly, depending on how often you need to reindex
If you find disk I/O for grep acceptable, why would it matter for vectors? They aren’t much bigger, are they?
Embeddings are huge compared to what you need with FTS, which generally has good locality, compresses extremely well, and permits sub-linear intersection algorithms and other tricks to make the most of your IOPS.
Regardless of vector size, you are unlikely to get more than one embedding per I/O operation with a vector approach. Even if you can fit more vectors into a block, there is no good way of arranging them to ensure efficient locality like you can with e.g. a postings list.
Thus off a 500K IOPS drive, given a 100ms execution window, your theoretical upper bound is 50K embeddings ranked, assuming actual ranking takes no time and no other disk operations are performed and you have only a single user.
Given you are more than likely comparing multiple embeddings per document, this carriage turns to a pumpkin pretty rapidly.
Information about how Bing text search works appears to be pretty sparse though.
One of the great mysteries to me right now is how ChatGPT search actually works.
It was Bing when they first launched it, but OpenAI have been investing a ton into their own search infrastructure since then. I can't figure out how much of it is Bing these days vs their own home-rolled system.
What's confusing is how secretive OpenAI are about it! I would personally value it a whole lot more if I understood how it works.
So maybe it's way more vector-based than I believe.
I'd expect any modern search engine to have aspects of vectors somewhere - some kind of hybrid BM25 + vectors thing, or using vectors for re-ranking after retrieving likely matches via FTS. That's different from being pure vectors though.
We open-sourced our impl just this week: https://github.com/with-logic/intent
We use Groq with gpt-oss-20b, which gives great results and only adds ~250ms to the processing pipeline.
If you use mini / flash models from OpenAI / Gemini, expect it to be 2.5s-3s of overhead.
Roughly, first there is the query analysis/manipulation phase where you might have NER, spell check, query expansion/relaxation etc
Then there is the selection phase, where you retrieve all items that are relevant. Sometimes people will bring in results from both text and vector based indices. Perhaps and additional layer to group results
Then finally you have the reranking layer using a cross encoder model which might even have some personalisation in the mix
Also, with vector search you might not need query expansion necessarily since semantic similarity does loose association. But every domain is unique and there’s only one way to find out
Really dislike this type of content...