> ## Documentation Index > Fetch the complete documentation index at: https://docs.graphify.com/llms.txt > Use this file to discover all available pages before exploring further. # Record useful findings > Save query outcomes and build lessons from previous investigations. Work memory records what you learned while using the graph. It is an optional layer over graph exploration, not a replacement for current source evidence. ## Save a result ```bash theme={null} graphify save-result --question "Where is email normalized?" \ --answer "normalize_email handles normalization before lookup." \ --nodes normalize_email --outcome useful ``` Use `useful`, `dead_end`, or `corrected` for the outcome. Replace the example with findings you actually verified in your project. ## Reflect on prior results ```bash theme={null} graphify reflect graphify reflect --if-stale ``` The default lessons file is under `graphify-out/reflections/LESSONS.md`. To relate lessons to the current graph: ```bash theme={null} graphify reflect --graph graphify-out/graph.json ``` This can produce a learning overlay that marks nodes as preferred, tentative, or contested. Query and explain output can show lesson hints, including a prompt to re-verify when source has changed. ## Keep lessons grounded A previously useful answer can become stale. Record the nodes involved, keep outcomes specific, and check the current source before using a lesson to justify a change. Share memory files only with people who can access their underlying source context.