Just spent the afternoon wrestling with Humata's "Ask" feature on a dense technical spec PDF, and I have to ask: is their "similar questions" suggestion engine trained on a completely different corpus, or is mine just uniquely broken? The cognitive dissonance between what I'm querying and what it *thinks* I should ask next is becoming a genuine workflow impediment.
For context, I was uploading API documentation for a legacy ERP system—think SOAP endpoints, custom field mappings, the usual middleware nightmare fuel. My first question was a precise one about the `UpdateInventory` endpoint's expected payload schema for partial updates. Humata's answer was actually decent, pulling the correct XML snippet from the doc. Then, the "similar questions" panel pops up, suggesting I ask:
* "What is the history of inventory management?"
* "How does cloud computing work?"
* "Summarize the document in one sentence."
This isn't just unhelpful; it's contextually absurd. It feels like a cheap keyword-matching layer slapped on top of the actual LLM, completely ignoring the document's specific technical domain. I've observed this pattern across different doc types:
* **CRM integration guides** yield suggestions like "What is customer service?"
* **OAuth2 flow documentation** prompts "Explain internet security."
* **Webhook payload examples** generate "What is a JSON?"
The implication is that the feature isn't analyzing the semantic context of the *current conversation* or even the specific document, but likely performing some naive bag-of-words extraction on the original query and matching it to a pre-baked list of "general" questions. For a tool marketed at technical document Q&A, this is a baffling oversight.
Has anyone else run into this, or found a workaround? I'm considering if there's a pattern—maybe it's worse with certain file formats, or documents under a certain page count. At this point, I'm half-tempted to inspect the network calls and block that panel entirely. It's more distracting than useful, which is a shame because the core answering capability, while sometimes hallucinatory, can be tuned. This feels like a product manager's checkbox feature ("AI suggests questions!") implemented without any understanding of how a technical user actually interacts with a document.
APIs are not magic.
Oh man, the ERP documentation example is painfully relatable. It's like the suggestion engine only sees "inventory" and starts desperately pulling from some intro-to-business freshman seminar notes it found in a ditch. I've seen the same thing with Kubernetes CRD specs, where asking about a specific schema validation rule prompts suggestions like "What is a container?" and "Explain Docker." It's a jarring, useless experience.
My theory is they're using a generic embedding model for that panel, completely separate from the fine-tuned one that actually answers your doc-specific questions. Saves cost, but destroys any semblance of context. Makes you feel like you're being gaslit by a bot that's only half-paying attention.