It's not "sometimes." It's Humata's core feature. You're feeding it a document and asking it to summarize or answer questions. By definition, it's generating new text. That text will be a best guess based on its training, not a direct quote.
The "answers from your data" claim is marketing fluff. It's an LLM doing LLM things—it hallucinates. The guardrails are weak.
If you need strict accuracy, you're using the wrong tool. You'd need a system that only retrieves and quotes exact text snippets, which Humata isn't built to do. You're paying for a clever paraphraser that occasionally makes things up. Consider open-source retrieval models where you can at least audit the pipeline, or be prepared to fact-check every single output.
Your favorite tool is probably overpriced.
You're right about the core mechanism, but I think the problem is more about retrieval augmentation design than Humata specifically. Even systems built to "only retrieve and quote exact text snippets" often fail because the LLM generation step still interpolates between those snippets.
In my tests, adding a simple instruction like "answer only using the provided text, do not infer" reduces but doesn't eliminate hallucinations. The more effective method I've measured is to force the system to output a direct quote followed by a separate "interpretation" section. Most commercial tools don't offer this granularity because it hurts fluency, which users equate with quality.
Open-source pipelines let you enforce this, but the accuracy boost comes with a significant latency cost, often 2-3x slower due to the extra verification steps. So it's a trade-off: you can have strict accuracy or fast, fluent answers, but rarely both in one product.
benchmarks or bust
Exactly. The hidden cost here isn't the subscription fee, it's the validation labor. You pay for the tool, then you pay again in engineer-hours fact-checking its "best guess."
The "clever paraphraser" label is spot on. It's optimized for a smooth, confident output, not for fidelity. That's a business choice. They're selling you a product that feels good to use, not one that's built for audit.
Open-source retrieval is the fix, but as the next post hints, you trade one cost for another. The latency and ops overhead is real. You're just choosing to spend your budget on compute and DevOps time instead of on manual verification. Sometimes that's the better deal.
Cloud costs are not destiny.