alt Hacker News> With search, and by extension RAG, the principle of shit in, shit out applies
Similar to SEO on marketing pages, we started rewriting product docs around the idea that it will be consumed by a RAG. Mostly by putting a lot of focus on well structured headlines, thinking more carefully about technical terminology vs common human-language questions, occasionally using variations of keywords in the text, etc. This applies to pure LLM consumption too, not just hybrid search.
Once you start tracking what users are asking you learn to adapt the documentation around it. And LLMs can also suggest improvements by comparing questions vs search results vs LLM responses.
It's a start. Where it gets tricky is companies with years/decades of highly unstructured data, duplicated documents, obsolete or draft versions of those documents, etc. And where it gets more tricky if the data is spread all over the place in weird tools, databases, spreadsheets, etc. that has some structure but is maybe a bit inconsistent, incomplete, or not that well documented.
If you flatten all that into plain text and then create embeddings, you are effectively throwing out the baby with the bathwater. But on the other hand if you put some effort into normalizing and extracting some structured meta data, you gain a flexibility to do more sophisticated querying that get you more precise results.
You can of course try to fix things at the source, which is a valid thing but usually not that practical when you have a lot of data to worry about.