Vector-driven search becomes most powerful when it learns from your actions. Each click, inclusion, or exclusion is a signal. Over time these signals form a feedback loop that adapts the search space to your needs.
Feedback as Meaning
In a traditional system, feedback is limited: you click a result or you do not. In a vector system, feedback can be richer. You can:- Add a result to reinforce its qualities.
- Partially add a result to keep some traits.
- Subtract a result to avoid its theme.
Each action reshapes the query vector. The system learns what you want, and you do not have to keep explaining it in words.
Iterative Refinement
A feedback loop allows you to start broad and narrow gradually. You see one result, decide it is close but not perfect, and partially subtract it. Then you see a new result that is closer. This makes search feel like a dialogue.You are not just filtering; you are steering.
Personalized Lenses
A lens is a saved vector that represents a style or focus you care about. You can build lenses such as:- "Technical but accessible"
- "Business strategy"
- "Creative exploration"
- "Historical context"
When you apply a lens, your query shifts into that style. This makes the system feel consistent. You do not need to re-create your preferences each time.
Building a Lens Library
You can build a library of lenses by capturing vectors from results you like. Over time, the library becomes a personal toolkit. You can also blend lenses to create new ones.Example:
- Blend "technical" and "narrative" to get in-depth explanations that still read smoothly.
- Blend "policy" and "systems thinking" to focus on governance with structural analysis.
Community Lenses
Lenses can be shared. A community of experts can build shared vectors for their domain. This democratizes expertise. Instead of learning all the jargon, you apply a lens that encodes the expertise.This is a shift from keyword knowledge to semantic knowledge. You do not need to know how to phrase a query if you can select the right lens.