search-optimization

To optimize AI search queries for precise code retrieval, use the `intent` parameter within the search tool, setting it to `find-implementation` to yield accurate code snippets rather than general results.

Reduce token consumption during AI knowledge search by using the `detail` parameter with progressive context strategies, starting with `minimal` and escalating to `contextual` or `full` only when necessary.

Yes, you can filter search results in Bluera Knowledge by utilizing the `stores` parameter, which focuses the search on specific data stores for more efficient and targeted information retrieval.

The `intent` parameter affects search ranking by tailoring results to specific needs; setting it to `find-pattern` prioritizes matching structural patterns within the search results over general relevance.

For usage tracking and definition lookup, start with the `minimal` detail level to conserve tokens, then switch to `contextual` if you need surrounding code usage or `full` for complete definitions.

General search queries return irrelevant results because they lack targeted filtering; apply the `limit` parameter to restrict the volume and use `intent` to align ranking with your specific retrieval goal.