I think we need to distinguish between "what the model received" and "why the model generated this answer." ThoughtDAG currently focuses on the former: accurately displaying the context of the incoming request and allowing users to modify it.
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Sometimes it’s just an awkward turn of phrase on my part that creates a wrinkle in the conversation. Sometimes agents identify that, and we can work together and direct that, but correction itself eventually loses competition to the original error.
I agree.
Most LLM tools (Claude Web, OpenAI, and their harness) offer re-editable questions. That is how I avoid such problems by myself.
In ThoughtDAG, you can re-edit questions by double-clicking the question. Or edit the answer by clicking the edit icon at the end of each answer text. Or.. you can just remove the connection or delete the node.
That would give you manageable context