The approach you worked out last month. The question you set aside last week. The distinction you spent an afternoon getting right. They are somewhere in your chat history—but hard to find when you need them again.
ThoughtDAG helps your next conversation pick up where your past thinking left off.
The animation uses fictional conversations to illustrate bringing back relevant history, choosing what carries forward, and continuing a question.
Does my AI tutor actually help learning?
Imagine returning to a study you started planning with AI. Earlier conversations contain a suggestion to track effort, a reminder to test again a week later, and an unrelated discussion of colours.
Bring the useful parts back to the current question. Leave out the colour discussion. Now you can ask something more specific: does the learner only perform better with help, or can they still do it later without AI?
That question can become its own branch. A follow-up can bring back a different piece of history, such as a plan to start with a small pilot.
Keep the useful paths connected
ThoughtDAG searches the supported local history you've connected. With recall enabled, you can inspect what was brought into a question, return to its source, and exclude material that no longer applies.
On the canvas, branch from a detail and connect useful conversation paths back to the question you're working on.
An example of branching and reconnecting a question. The diagram does not claim a research result.
Let your past thinking help you now.
Explore ThoughtDAG · Set up history and recall
Disclosure: I maintain ThoughtDAG. History depends on connected sources and indexing coverage. Retrieved material still needs to be checked against the present situation. The scenario is illustrative.
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