Thanks for sharing. TIL about rerankers.

Chunking strategy is a big issue. I found acceptable results by shoving large texts to to gemini flash and have it summarize and extract chunks instead of whatever text splitter I tried. I use the method published by Anthropic https://www.anthropic.com/engineering/contextual-retrieval i.e. include full summary along with chunks for each embedding.

I also created a tool to enable the LLM to do vector search on its own .

I do not use Langchain or python.. I use Clojure+ LLMs' REST APIs.

I have a RAG setup that doesn't work on documents but other data points that we use for generation (the original data is call recordings but it is heavily processed to just a few text chunks). Instead of a reranker model we do vector search and then simply ask GPT-5 in an extra call which of the results is the most relevant to the input question. Is there an advantage to actual reranker models rather than using a generic LLM?

They say the chunker is the most important part, but theirs looks rudimentary: https://github.com/agentset-ai/agentset/blob/main/packages/e...

That is, there is nothing here that one could not easily write without a library.

> What moved the needle: Query Generation

What does query generation mean in this context, it’s probably not SQL queries right?

Exactly what kind of processing was done? Your pipeline is a function of the use case, lest you overengineer…

Similar writeup I did about 1.5 years ago for processing millions of (technical) pages for RAG. Lots has stayed the same it seems

https://jakobs.dev/learnings-ingesting-millions-pages-rag-az...

I concur:

The big LLM-based rerankers (e.g. Qwen3-reranker) are what you always wanted your cross-encoder to be, and I highly recommend giving them a try. Unfortunately they're also quite computationally expensive.

Your metadata/tabular data often contains basic facts that a human takes for granted, but which aren't repeated in every text chunk - injecting it can help a lot in making the end model seem less clueless.

The point about queries that don't work with simple RAG (like "summarize the most recent twenty documents") is very important to keep in mind. We made our UI very search-oriented and deemphasized the chat, to try to communicate to users that search is what's happening under the hood - the model only sees what you see.

Embedding based RAG will always just be OK at best. It is useful for little parts of a chain or tech demos, but in real life use it will always falter.

The point about synthetic query generation is good. We found users had very poor queries, so we initially had the LLM generate synthetic queries. But then we found that the results could vary widely based on the specific synthetic query it generated, so we had it create three variants (all in one LLM call, so that you can prompt it to generate a wide variety, instead of getting three very similar ones back), do parallel search, and then use reciprocal rank fusion to combine the list into a set of broadly strong performers. For the searches we use hybrid dense + sparse bm25, since dense doesn't work well for technical words.

This, combined with a subsequent reranker, basically eliminated any of our issues on search.

> Reranking: the highest value 5 lines of code you'll add. The chunk ranking shifted a lot. More than you'd expect. Reranking can many times make up for a bad setup if you pass in enough chunks. We found the ideal reranker set-up to be 50 chunk input -> 15 output.

What is re-ranking in the context of RAG? Why not just show the code if it’s only 5 lines?

They should've tested other embedding models, there are better ones than openai's (and cheaper)

Not here to schlep for AWS but S3 Vectors is hands down the SOTA here. That combined with a Bedrock Knowledge Base to handle Discovery/Rebalance tasks makes for the simplest implementation on the Market.

Once Bedrock KB backed by S3 Vectors is released from Beta it'll eat everybody's lunch.

I find it interesting that so many services and tools were investigated except for embedding models. I would have thought that's one of the biggest levers.