Why is defining relevance still the hardest part of building AI features?

As more teams build AI agents, search, and personalized feeds, one problem keeps surfacing.
Not generation.
Not model quality.

It’s retrieval and ranking. Deciding what information should show up and in what order.

Most teams solve this by stitching together systems. Vector search for meaning. Keyword search for precision. Custom logic for business rules. Over time, relevance logic spreads everywhere and becomes hard to change.

@Shaped approaches this differently.

It treats relevance as something you define in one place. Retrieval, filtering, scoring, and ordering are expressed as a single query, instead of being scattered across services and scripts.

Some teams have replaced thousands of lines of ranking and maintenance code with a few dozen lines, while still serving results in real time.

As AI systems become more autonomous, this layer starts to matter more than the model itself. Generating answers is easy. Choosing what deserves attention is not.

Curious, where does retrieval or ranking slow your team down the most today?