The core value proposition of a platform like Poe, especially for power users in marketing or analytics, is the ability to build upon previous conversations. The current implementation of search within bot history fundamentally fails at this, rendering historical data nearly unactionable for any serious workflow.
My primary issue is the lack of contextual retrieval. A simple keyword search returns a list of message snippets, completely divorced from the broader conversation thread. For example, searching for "attribution model" might show me a message where I mentioned the term, but it provides zero insight into:
* Which bot I was discussing it with (Claude vs. GPT-4 yields vastly different outputs).
* The specific question I asked that prompted that response.
* The subsequent refinements or follow-ups that gave the term its full context.
This makes reconstructing a valuable train of thought or reusing a specific prompt configuration impractical. It's a data point without metadata, which in any marketing automation context is considered useless.
Furthermore, the search appears to be surface-level only. It does not seem to effectively parse the AI's responses, only user prompts. If I need to find a conversation where a bot generated a specific block of HubSpot API code or a particular analytics formula, I am out of luck. The search functionality needs to treat both sides of the dialogue as first-class, searchable text.
From a data perspective, this creates a significant inefficiency. The platform is sitting on a goldmine of structured interaction data but provides only the most primitive tool to access it. For professionals who rely on comparing outputs, iterating on queries, and building libraries of effective prompts, this is a major roadblock. The current feature feels like an afterthought rather than a core component of knowledge management.
Show me the data