pattern playground

Filtering

Added

The narrowing of a collection to items that match the actor’s criteria, balancing precision with the risk of hiding relevant results.

Building a filter

Placement

  • inline, popover, drawer…
  • pairing with table header

…

When simple filter fails, the attribute search query is passed to LLM to find a match in the available attributes.

If that fails, users can delegate filtering to an agent via a task. Agent can have access to data beyond the attributes and values visible to the user.

Flow 1

  1. Enter a query that does not have a match in the available attributes
  2. Create a task to handle it
  3. Show “thinking…” placeholder
  4. Replace it with a task result reference.

Flow 2

  1. Create a task outside filtering whose result can be used to filter the collection.
  2. Follow steps 3-4 from Flow 1.
LLM-assisted filter
  • Command menu — a palette over attributes and values, with hierarchical drill-in and AI fallback
  • Combobox — the value-picker inside each active filter chip uses a combobox to narrow available values

To-do

Complementary patterns to capture: constrained natural language builder, rules, search.

Resources & references

Consequences

  • the visible set is the actor's criteria made operative, and the criteria stay on the surface as chips or clauses so the actor can read what they are not being shown
  • an empty result is a dead end the actor reaches still holding their intent, and an over-full one means the criteria they have aren't the criteria the attributes can express — both are where narrowing stops being enough

Related patterns

Precedes

Complements

  • Attribute visibility — surface then operate: an attribute made visible becomes a predicate to filter by
  • Autocomplete — autocomplete within filter value selection
  • Grouping — narrows what groups show up
  • Sorting — orders what filtering lets through
  • Selection — the imperative dual; filter narrows by predicate, selection narrows by enumeration
  • Pyramid — for large collections
  • Searching — often used after a search to refine results; in AI-search, filters are the output of the search query

Preceded by

  • Embedded intelligence — natural language queries translated into structured filters
  • Tag — an applied label is a handle: whatever has been tagged becomes a facet to narrow by, which is where the value of tagging is actually collected

Enabled by

  • Bounded choice — each filter condition is a bounded choice over a facet's values