10
Relevance Feedback
Did you know that Google search results used to have 👍/👎 buttons?
It was the way to collect signals from users and improve the ranking.
Do you know why you don't see those buttons anymore?
People are either too lazy to click or they abuse the buttons to their advantage.
In 2026, however, real people don't have to rank raw search results anymore - there are all sorts of AI in the middle.
And here we are talking not only about Agents.
From a raw retrieval to a final answer, there are multiple stages of progressively smarter re-ranking models,
each of which refines the result a little bit better than the previous one.
Each of those models also costs a little more than the model in the previous stage.
And so far, the process of re-ranking was linear: for better results, you need to pre-select more candidates.
That means that you pay proportionally more for compute.
Relevance feedback changes this pattern.
Instead of linear re-ranking, you can take feedback information and reiterate the retrieval process to source better candidates.
And for the first time, we implemented a relevance feedback mechanism directly in the vector search engine.
For the same or less number of calls of the expensive model, you can get better results.
And unlike previous methods, we don't need to re-score hundreds of candidates to extract the signal for relevance feedback,
meaning that this approach is efficient enough to be used in real-time applications.
