workflow-research

To avoid scattered research notes, structured information gathering uses a deterministic chain: memory recall, optional search, knowledge-management checks, and saving findings. This ensures captured insights are traceable and auditable.

Memory-first recall checks existing memory for stored knowledge before performing external searches during competitive analysis. It applies recency checks to ensure memory freshness, only triggering optional searches when needed.

To start a research session and save findings to memory, simply instruct the AI to begin gathering information on a topic. The workflow automatically applies knowledge-management checks and remembers steps to ensure findings are saved.

Yes, you can use this for topic exploration and decision prep without external searches. The deterministic chain prioritizes memory recall first, making external lookups optional when existing knowledge is sufficient.

The best way to capture insights during deep digging is using a structured workflow that automates knowledge capture. It guides memory recall before external lookups and saves findings in a structured memory system for later recall.

Yes, this workflow applies recency checks to stored knowledge before making decisions. It evaluates memory freshness during the recall phase to determine if optional searches are needed to update findings.