search-requirements

To find past requirements, enter a natural language query into the search interface. The system applies keyword, tag, component, and date filters to your indexed requirements storage, ranking results by relevance and returning summaries with identifiers and metadata.

The best way to retrieve previous architectural decisions is to search a centralized requirements knowledge base using semantic search. This approach applies relevance-based ranking and multiple filters to quickly locate summaries, identifiers, and contextual metadata from past work.

Yes, you can filter past requirements search results by component and date range. The search mechanism supports keyword, tag, component, and date filters, ranking the retrieved requirements by relevance to present the most applicable summaries and identifiers.

Yes, you need configured requirements storage and a search index to search past requirements effectively. This setup is required to apply fast, index-based search across the repository and return top ranked results with summaries and metadata quickly.

Relevance-based ranking works by evaluating your search query against applied keyword, tag, component, and date filters within the requirements index. It scores and orders matching records, presenting the top results as summaries with identifiers and contextual metadata.